CdaAnnotator
+
+
+Annotates a Clinical Document Architecture (CDA) document.
+ + +| PARAMETER | +DESCRIPTION | +
|---|---|
+ cda_data
+ |
+
+
+
+ The CDA document data. +
+
+ TYPE:
+ |
+
+ fallback
+ |
+
+
+
+ The fallback value. Defaults to "LLM". +
+
+ TYPE:
+ |
+
| ATTRIBUTE | +DESCRIPTION | +
|---|---|
clinical_document |
+
+
+
+ The CDA document data. +
+
+ TYPE:
+ |
+
fallback |
+
+
+
+ The fallback value. +
+
+ TYPE:
+ |
+
problem_list |
+
+
+
+ The list of problems extracted from the CDA document. +
+
+ TYPE:
+ |
+
medication_list |
+
+
+
+ The list of medications extracted from the CDA document. +
+
+ TYPE:
+ |
+
allergy_list |
+
+
+
+ The list of allergies extracted from the CDA document. +
+
+ TYPE:
+ |
+
note |
+
+
+
+ The note extracted from the CDA document. +
+
+ TYPE:
+ |
+
| METHOD | +DESCRIPTION | +
|---|---|
from_dict |
+
+
+
+ Dict): Creates a CdaAnnotator instance from a dictionary. + |
+
from_xml |
+
+
+
+ str): Creates a CdaAnnotator instance from an XML string. + |
+
add_to_problem_list |
+
+
+
+ List[ProblemConcept], overwrite: bool = False) -> None: Adds a list of problem concepts to the problems section. + |
+
export |
+
+
+
+ bool = True) -> str: Exports the CDA document as an XML string. + |
+
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add_to_medication_list(medications, overwrite=False)
+
+Adds medications to the medication list.
+ + +| PARAMETER | +DESCRIPTION | +
|---|---|
+ medications
+ |
+
+
+
+ A list of MedicationConcept objects representing the medications to be added. +
+
+ TYPE:
+ |
+
+ overwrite
+ |
+
+
+
+ If True, the existing medication list will be overwritten. Defaults to False. +
+
+ TYPE:
+ |
+
| RETURNS | +DESCRIPTION | +
|---|---|
+
+ None
+
+ |
+
+
+
+ None + |
+
healthchain/cda_parser/cdaannotator.pyadd_to_problem_list(problems, overwrite=False)
+
+Adds a list of problem lists to the problems section.
+ + +| PARAMETER | +DESCRIPTION | +
|---|---|
+ problems
+ |
+
+
+
+ A list of problem concepts to be added. +
+
+ TYPE:
+ |
+
+ overwrite
+ |
+
+
+
+ If True, the existing problem list will be overwritten. +Defaults to False. +
+
+ TYPE:
+ |
+
| RETURNS | +DESCRIPTION | +
|---|---|
+
+ None
+
+ |
+
+
+
+ None + |
+
healthchain/cda_parser/cdaannotator.pyexport(pretty_print=True)
+
+Exports CDA document as an XML string
+ +healthchain/cda_parser/cdaannotator.pyfrom_dict(data)
+
+
+ classmethod
+
+
+Creates an instance of the class from a dictionary.
+ + +| PARAMETER | +DESCRIPTION | +
|---|---|
+ data
+ |
+
+
+
+ The dictionary containing the dictionary representation of the cda xml (using xmltodict.parse). +
+
+ TYPE:
+ |
+
| RETURNS | +DESCRIPTION | +
|---|---|
+ CdaAnnotator
+ |
+
+
+
+ An instance of the class initialized with the data from the dictionary. +
+
+ TYPE:
+ |
+
healthchain/cda_parser/cdaannotator.pyfrom_xml(data)
+
+
+ classmethod
+
+
+Creates an instance of the CDAAnnotator class from an XML string.
+ + +| PARAMETER | +DESCRIPTION | +
|---|---|
+ data
+ |
+
+
+
+ The XML string representing the CDA document. +
+
+ TYPE:
+ |
+
| RETURNS | +DESCRIPTION | +
|---|---|
+ CDAAnnotator
+ |
+
+
+
+ An instance of the CDAAnnotator class initialized with the parsed CDA data. +
+
+ TYPE:
+ |
+
healthchain/cda_parser/cdaannotator.pycheck_for_entry_observation(entry)
+
+Checks if the given entry contains an observation.
+ + +| PARAMETER | +DESCRIPTION | +
|---|---|
+ entry
+ |
+
+
+
+ The entry to check. +
+
+ TYPE:
+ |
+
| RETURNS | +DESCRIPTION | +
|---|---|
+
+ bool
+
+ |
+
+
+
+ True if the entry contains an observation, False otherwise. + |
+
healthchain/cda_parser/cdaannotator.pycheck_has_template_id(section, template_id)
+
+Check if the given section has a matching template ID.
+ + +| PARAMETER | +DESCRIPTION | +
|---|---|
+ section
+ |
+
+
+
+ The section to check. +
+
+ TYPE:
+ |
+
+ template_id
+ |
+
+
+
+ The template ID to match. +
+
+ TYPE:
+ |
+
| RETURNS | +DESCRIPTION | +
|---|---|
+
+ bool
+
+ |
+
+
+
+ True if the section has a matching template ID, False otherwise. + |
+
healthchain/cda_parser/cdaannotator.pyget_time_range_from_cda_value(value)
+
+Converts a dictionary representing a time range from a CDA value into a Range object.
+ + +| PARAMETER | +DESCRIPTION | +
|---|---|
+ value
+ |
+
+
+
+ A dictionary representing the CDA value. +
+
+ TYPE:
+ |
+
| RETURNS | +DESCRIPTION | +
|---|---|
+ Range
+ |
+
+
+
+ A Range object representing the time range. +
+
+ TYPE:
+ |
+
healthchain/cda_parser/cdaannotator.pyget_value_from_entry_relationship(entry_relationship)
+
+Retrieves the values from the given entry_relationship.
+ + +| PARAMETER | +DESCRIPTION | +
|---|---|
+ entry_relationship
+ |
+
+
+
+ The entry_relationship object to extract values from. +
+
+ TYPE:
+ |
+
| RETURNS | +DESCRIPTION | +
|---|---|
+
+ List
+
+ |
+
+
+
+ A list of values extracted from the entry_relationship. + |
+
healthchain/cda_parser/cdaannotator.pyEncounterDischargeContext
+
+
+
+ Bases: BaseHookContext
Workflow: +This hook is triggered during the discharge process for typically inpatient encounters. It can be invoked +at any point from the start to the end of the discharge process. The purpose is to allow hook services to +intervene in various aspects of the discharge decision. This includes verifying discharge medications, +ensuring continuity of care planning, and verifying necessary documentation for discharge processing.
+ + +| ATTRIBUTE | +DESCRIPTION | +
|---|---|
userId |
+
+
+
+ REQUIRED. The ID of the current user, expected to be a Practitioner or PractitionerRole. + For example, 'Practitioner/123'. +
+
+ TYPE:
+ |
+
patientId |
+
+
+
+ REQUIRED. The FHIR Patient.id of the patient being discharged. +
+
+ TYPE:
+ |
+
encounterId |
+
+
+
+ REQUIRED. The FHIR Encounter.id of the encounter being ended. +
+
+ TYPE:
+ |
+
Documentation: https://cds-hooks.org/hooks/encounter-discharge/
+ + + + + + +healthchain/models/hooks/encounterdischarge.pyOrderSelectContext
+
+
+
+ Bases: BaseHookContext
Workflow: The order-select hook occurs after the clinician selects the order and before signing. +This hook occurs when a clinician initially selects one or more new orders from a list of +potential orders for a specific patient (including orders for medications, procedures, labs +and other orders). The newly selected order defines that medication, procedure, lab, etc, +but may or may not define the additional details necessary to finalize the order.
+ + +| ATTRIBUTE | +DESCRIPTION | +
|---|---|
userId |
+
+
+
+ REQUIRED. An identifier of the current user, in the format [ResourceType]/[id], + where ResourceType is either 'Practitioner' or 'PractitionerRole'. Examples: 'PractitionerRole/123', + 'Practitioner/abc'. +
+
+ TYPE:
+ |
+
patientId |
+
+
+
+ REQUIRED. The FHIR Patient.id representing the current patient in context. +
+
+ TYPE:
+ |
+
encounterId |
+
+
+
+ OPTIONAL. The FHIR Encounter.id representing the current encounter in context, + if applicable. +
+
+ TYPE:
+ |
+
selections |
+
+
+
+ REQUIRED. A list of the FHIR id(s) of the newly selected orders, referencing resources + in the draftOrders Bundle. Example: 'MedicationRequest/103'. +
+
+ TYPE:
+ |
+
draftOrders |
+
+
+
+ REQUIRED. A Bundle of FHIR request resources with a draft status, representing all unsigned + orders from the current session, including newly selected orders. +
+
+ TYPE:
+ |
+
Documentation: https://cds-hooks.org/hooks/order-select/
+ + + + + + +healthchain/models/hooks/orderselect.pyOrderSignContext
+
+
+
+ Bases: BaseHookContext
Workflow: +The order-sign hook is triggered when a clinician is ready to sign one or more orders for a patient. +This includes orders for medications, procedures, labs, and other orders. It is one of the last workflow +events before an order is promoted from a draft status. The context includes all order details such as +dose, quantity, route, etc., even though the order is still in a draft status. This hook is also applicable +for re-signing revised orders, which may have a status other than 'draft'. The hook replaces the +medication-prescribe and order-review hooks.
+ + +| ATTRIBUTE | +DESCRIPTION | +
|---|---|
userId |
+
+
+
+ REQUIRED. The ID of the current user, expected to be of type 'Practitioner' or 'PractitionerRole'. + Examples include 'PractitionerRole/123' or 'Practitioner/abc'. +
+
+ TYPE:
+ |
+
patientId |
+
+
+
+ REQUIRED. The FHIR Patient.id representing the current patient in context. +
+
+ TYPE:
+ |
+
encounterId |
+
+
+
+ OPTIONAL. The FHIR Encounter.id of the current encounter in context. +
+
+ TYPE:
+ |
+
draftOrders |
+
+
+
+ REQUIRED. A Bundle of FHIR request resources with a draft status, representing orders that + aren't yet signed from the current ordering session. +
+
+ TYPE:
+ |
+
Documentation: https://cds-hooks.org/hooks/order-sign/
+ + + + + + +healthchain/models/hooks/ordersign.pyPatientViewContext
+
+
+
+ Bases: BaseHookContext
Workflow: The user has just opened a patient's record; typically called only once at the beginning of a user's +interaction with a specific patient's record.
+ + +| ATTRIBUTE | +DESCRIPTION | +
|---|---|
userId |
+
+
+
+ An identifier of the current user, in the format [ResourceType]/[id], + where ResourceType is one of 'Practitioner', 'PractitionerRole', 'Patient', + or 'RelatedPerson'. Examples: 'Practitioner/abc', 'Patient/123'. +
+
+ TYPE:
+ |
+
patientId |
+
+
+
+ The FHIR Patient.id representing the current patient in context. +
+
+ TYPE:
+ |
+
encounterId |
+
+
+
+ The FHIR Encounter.id representing the current encounter in context, + if applicable. This field is optional. +
+
+ TYPE:
+ |
+
Documentation: https://cds-hooks.org/hooks/patient-view/
+ + + + + + +healthchain/models/hooks/patientview.pyhttps://cds-hooks.org/specification/current/#discovery
+ + + + + + + + +CDSService
+
+
+
+ Bases: BaseModel
A model representing a CDS service configuration.
+ + +| ATTRIBUTE | +DESCRIPTION | +
|---|---|
hook |
+
+
+
+ The hook this service should be invoked on. This should correspond to one of the predefined hooks. +
+
+ TYPE:
+ |
+
title |
+
+
+
+ The human-friendly name of this service. It is recommended to provide this for better usability. +
+
+ TYPE:
+ |
+
description |
+
+
+
+ A detailed description of what this service does and its purpose within the CDS framework. +
+
+ TYPE:
+ |
+
id |
+
+
+
+ The unique identifier of this service. It forms part of the URL as {baseUrl}/cds-services/{id}. +
+
+ TYPE:
+ |
+
prefetch |
+
+
+
+ Optional FHIR queries that the service requests the CDS Client to perform + and provide on each service call. Keys describe the type of data and values are the actual FHIR query strings. +
+
+ TYPE:
+ |
+
usageRequirements |
+
+
+
+ Human-friendly description of any preconditions for the use of this CDS service. +
+
+ TYPE:
+ |
+
Documentation: https://cds-hooks.org/specification/current/#response
+ + + + + + +healthchain/models/responses/cdsdiscovery.pyCDSServiceInformation
+
+
+
+ Bases: BaseModel
A CDS Service is discoverable via a stable endpoint by CDS Clients. The Discovery endpoint includes information such as a +description of the CDS Service, when it should be invoked, and any data that is requested to be prefetched.
+ + + + + + +healthchain/models/responses/cdsdiscovery.pyThis is not compulsary
+https://cds-hooks.org/specification/current/#feedback
+ + + + + + + + +CDSFeedback
+
+
+
+ Bases: BaseModel
A feedback endpoint enables suggestion tracking & analytics. +A CDS Service MAY support a feedback endpoint; a CDS Client SHOULD be capable of sending feedback.
+ + +| ATTRIBUTE | +DESCRIPTION | +
|---|---|
card |
+
+
+
+ The card.uuid from the CDS Hooks response. Uniquely identifies the card. +
+
+ TYPE:
+ |
+
outcome |
+
+
+
+ The outcome of the action, either 'accepted' or 'overridden'. +
+
+ TYPE:
+ |
+
acceptedSuggestions |
+
+
+
+ An array of accepted suggestions, required if the outcome is 'accepted'. +
+
+ TYPE:
+ |
+
overrideReason |
+
+
+
+ The reason for overriding, including any coding and comments. +
+
+ TYPE:
+ |
+
outcomeTimestamp |
+
+
+
+ The ISO8601 timestamp of when the action was taken on the card. +
+
+ TYPE:
+ |
+
Documentation: https://cds-hooks.org/specification/current/#feedback
+ + + + + + +healthchain/models/responses/cdsfeedback.pyEHRClient
+
+
+
+ Bases: BaseClient
healthchain/clients/ehrclient.py100 +101 +102 +103 +104 +105 +106 +107 +108 +109 +110 +111 +112 +113 +114 +115 +116 +117 +118 +119 +120 +121 +122 +123 +124 +125 +126 +127 +128 +129 +130 +131 +132 +133 +134 +135 +136 +137 +138 +139 +140 +141 +142 +143 +144 +145 +146 +147 +148 +149 +150 +151 +152 +153 +154 +155 +156 +157 +158 +159 +160 +161 +162 +163 +164 +165 +166 +167 +168 +169 +170 +171 +172 +173 +174 +175 +176 +177 +178 +179 +180 +181 +182 +183 +184 +185 +186 +187 +188 +189 +190 +191 +192 +193 +194 +195 | |
__init__(func, workflow, strategy, timeout=10.0)
+
+Initializes the EHRClient with a data generator function and optional workflow and use case. +Should be a subclass of BaseUseCase. Example - ClinicalDecisionSupport()
+ + +| PARAMETER | +DESCRIPTION | +
|---|---|
+ func
+ |
+
+
+
+ A function to generate data for requests. +
+
+ TYPE:
+ |
+
+ workflow
+ |
+
+
+
+ The workflow context to apply to the data generator. +
+
+ TYPE:
+ |
+
+ strategy
+ |
+
+
+
+ The strategy object to construct requests based on the generated data. +
+
+ TYPE:
+ |
+
+ timeout
+ |
+
+
+
+ The maximum time in seconds to wait for a response from the server. This parameter determines how long the client will wait before considering a request timed out. +
+
+ TYPE:
+ |
+
healthchain/clients/ehrclient.pygenerate_request(*args, **kwargs)
+
+Generates a request using the data produced by the data generator function, +and appends it to the internal request queue.
+Parameters:
+ *args (Any): Positional arguments passed to the data generator function.
+ **kwargs (Any): Keyword arguments passed to the data generator function.
+
+Raises:
+ ValueError: If the use case is not configured.
+healthchain/clients/ehrclient.pysend_request(url)
+
+
+ async
+
+
+Sends all queued requests to the specified URL and collects the responses.
+Parameters:
+ url (str): The URL to which the requests will be sent.
+Returns:
+ List[dict]: A list of JSON responses from the server.
+Notes:
+ This method logs errors rather than raising them, to avoid interrupting the batch processing of requests.
+healthchain/clients/ehrclient.pyehr(func=None, *, workflow, num=1)
+
+A decorator that wraps around a data generator function and returns an EHRClient
+ + +| PARAMETER | +DESCRIPTION | +
|---|---|
+ func
+ |
+
+
+
+ The function to be decorated. If None, this allows the decorator to + be used with arguments. +
+
+ TYPE:
+ |
+
+ workflow
+ |
+
+
+
+ The workflow identifier which should match an item in the Workflow enum. + This specifies the context in which the EHR function will operate. +
+
+ TYPE:
+ |
+
+ num
+ |
+
+
+
+ The number of requests to generate in the queue; defaults to 1. +
+
+ TYPE:
+ |
+
| RETURNS | +DESCRIPTION | +
|---|---|
+ Callable
+ |
+
+
+
+ A decorated callable that incorporates EHR functionality or the decorator itself + if 'func' is None, allowing it to be used as a parameterized decorator. +
+
+ TYPE:
+ |
+
| RAISES | +DESCRIPTION | +
|---|---|
+
+ ValueError
+
+ |
+
+
+
+ If the workflow does not correspond to any defined enum or if use case is not configured. + |
+
+
+ NotImplementedError
+
+ |
+
+
+
+ If the use case class is not one of the supported types. + |
+
@ehr(workflow='patient-view', num=2) +def generate_data(self, config): + # Function implementation
+healthchain/clients/ehrclient.py20 +21 +22 +23 +24 +25 +26 +27 +28 +29 +30 +31 +32 +33 +34 +35 +36 +37 +38 +39 +40 +41 +42 +43 +44 +45 +46 +47 +48 +49 +50 +51 +52 +53 +54 +55 +56 +57 +58 +59 +60 +61 +62 +63 +64 +65 +66 +67 +68 +69 +70 +71 +72 +73 +74 +75 +76 +77 +78 +79 +80 +81 +82 +83 +84 +85 +86 +87 +88 +89 +90 +91 +92 +93 +94 +95 +96 +97 | |
BaseComponent
+
+
+
+ Bases: Generic[T], ABC
Abstract base class for all components in the pipeline.
+This class should be subclassed to create specific components. +Subclasses must implement the call method.
+ + + + + + +healthchain/pipeline/components/base.py__call__(data)
+
+
+ abstractmethod
+
+
+Process the input data and return the processed data.
+ + +| PARAMETER | +DESCRIPTION | +
|---|---|
+ data
+ |
+
+
+
+ The input data to be processed. +
+
+ TYPE:
+ |
+
| RETURNS | +DESCRIPTION | +
|---|---|
+
+ DataContainer[T]
+
+ |
+
+
+
+ DataContainer[T]: The processed data. + |
+
healthchain/pipeline/components/base.pyComponent
+
+
+
+ Bases: BaseComponent[T]
A concrete implementation of the BaseComponent class.
+This class can be used as a base for creating specific components +that do not require any additional processing logic.
+ + +| METHOD | +DESCRIPTION | +
|---|---|
__call__ |
+
+
+
+ DataContainer[T]) -> DataContainer[T]: +Process the input data and return the processed data. +In this implementation, the input data is returned unmodified. + |
+
healthchain/pipeline/components/base.pyHFTransformer
+
+
+
+ Bases: BaseComponent[str]
A component that integrates Hugging Face transformers models into the pipeline.
+This component allows using any Hugging Face model and task within the pipeline +by wrapping the transformers.pipeline API. The model outputs are stored in the +document's model_outputs container under the "huggingface" source key.
+Note that this component is only recommended for non-conversational language tasks. +For chat-based tasks, consider using LangChainLLM instead.
+ + +| PARAMETER | +DESCRIPTION | +
|---|---|
+ pipeline
+ |
+
+
+
+ A pre-configured HuggingFace pipeline object to use for inference. +Must be an instance of transformers.pipelines.base.Pipeline. +
+
+ TYPE:
+ |
+
| ATTRIBUTE | +DESCRIPTION | +
|---|---|
task |
+
+
+
+ The task name of the underlying pipeline, e.g. "sentiment-analysis", "ner". +Automatically extracted from the pipeline object. +
+
+ TYPE:
+ |
+
| RAISES | +DESCRIPTION | +
|---|---|
+
+ ImportError
+
+ |
+
+
+
+ If the transformers package is not installed + |
+
+
+ TypeError
+
+ |
+
+
+
+ If pipeline is not a valid HuggingFace Pipeline instance + |
+
++++++Initialize for sentiment analysis
+from transformers import pipeline +nlp = pipeline("sentiment-analysis", model="distilbert-base-uncased-finetuned-sst-2-english") +component = HFTransformer(pipeline=nlp) +doc = component(doc) # Analyzes sentiment of doc.data
+Or use the factory method
+component = HFTransformer.from_model_id( +... model="facebook/bart-large-cnn", +... task="summarization", +... max_length=130, +... min_length=30, +... do_sample=False +... ) +doc = component(doc) # Generates summary of doc.data
+
healthchain/pipeline/components/integrations.py136 +137 +138 +139 +140 +141 +142 +143 +144 +145 +146 +147 +148 +149 +150 +151 +152 +153 +154 +155 +156 +157 +158 +159 +160 +161 +162 +163 +164 +165 +166 +167 +168 +169 +170 +171 +172 +173 +174 +175 +176 +177 +178 +179 +180 +181 +182 +183 +184 +185 +186 +187 +188 +189 +190 +191 +192 +193 +194 +195 +196 +197 +198 +199 +200 +201 +202 +203 +204 +205 +206 +207 +208 +209 +210 +211 +212 +213 +214 +215 +216 +217 +218 +219 +220 +221 +222 +223 +224 +225 +226 +227 +228 +229 +230 +231 +232 +233 +234 +235 +236 +237 +238 +239 +240 +241 | |
__call__(doc)
+
+Process the document using the Hugging Face pipeline. Adds outputs to .model_outputs['huggingface'].
+ +healthchain/pipeline/components/integrations.py__init__(pipeline)
+
+Initialize with a pre-configured HuggingFace pipeline.
+ + +| PARAMETER | +DESCRIPTION | +
|---|---|
+ pipeline
+ |
+
+
+
+ A pre-configured HuggingFace pipeline object from transformers.pipeline(). + Must be an instance of transformers.pipelines.base.Pipeline. +
+
+ TYPE:
+ |
+
| RAISES | +DESCRIPTION | +
|---|---|
+
+ ImportError
+
+ |
+
+
+
+ If transformers package is not installed + |
+
+
+ TypeError
+
+ |
+
+
+
+ If pipeline is not a valid HuggingFace Pipeline instance + |
+
healthchain/pipeline/components/integrations.pyfrom_model_id(model, task, **kwargs)
+
+
+ classmethod
+
+
+Create a transformer component from a model identifier.
+Factory method that initializes a HuggingFace pipeline with the specified model and task, +then wraps it in a HFTransformer component.
+ + +| PARAMETER | +DESCRIPTION | +
|---|---|
+ model
+ |
+
+
+
+ The model identifier or path to load. Can be: +- A model ID from the HuggingFace Hub (e.g. "bert-base-uncased") +- A local path to a saved model +
+
+ TYPE:
+ |
+
+ task
+ |
+
+
+
+ The task to run (e.g. "text-classification", "token-classification", "summarization") +
+
+ TYPE:
+ |
+
+ **kwargs
+ |
+
+
+
+ Additional configuration options passed to transformers.pipeline() +Common options include: +- device: Device to run on ("cpu", "cuda", etc.) +- batch_size: Batch size for inference +- model_kwargs: Dict of model-specific args +
+
+ TYPE:
+ |
+
| RETURNS | +DESCRIPTION | +
|---|---|
+ HFTransformer
+ |
+
+
+
+ Initialized transformer component wrapping the pipeline +
+
+ TYPE:
+ |
+
| RAISES | +DESCRIPTION | +
|---|---|
+
+ TypeError
+
+ |
+
+
+
+ If invalid kwargs are passed to pipeline initialization + |
+
+
+ ValueError
+
+ |
+
+
+
+ If pipeline initialization fails for any other reason + |
+
+
+ ImportError
+
+ |
+
+
+
+ If transformers package is not installed + |
+
healthchain/pipeline/components/integrations.pyLangChainLLM
+
+
+
+ Bases: BaseComponent[str]
A component that integrates LangChain chains into the pipeline.
+This component allows using any LangChain chain within the pipeline by wrapping +the chain's invoke method. The chain outputs are stored in the document's +model_outputs container under the "langchain" source key.
+ + +| PARAMETER | +DESCRIPTION | +
|---|---|
+ chain
+ |
+
+
+
+ The LangChain chain to run on the document text. +Must be a Runnable object from the LangChain library. +
+
+ TYPE:
+ |
+
+ task
+ |
+
+
+
+ The task name to use when storing outputs, e.g. "summarization", "chat". +Used as key to organize model outputs in the document's model container. +
+
+ TYPE:
+ |
+
+ **kwargs
+ |
+
+
+
+ Additional parameters to pass to the chain's invoke method. +These are forwarded directly to the chain's invoke() call. +
+
+ TYPE:
+ |
+
| RAISES | +DESCRIPTION | +
|---|---|
+
+ TypeError
+
+ |
+
+
+
+ If chain is not a LangChain Runnable object or if invalid kwargs are passed + |
+
+
+ ValueError
+
+ |
+
+
+
+ If there is an error during chain invocation + |
+
+
+ ImportError
+
+ |
+
+
+
+ If langchain-core package is not installed + |
+
++++++from langchain_core.prompts import ChatPromptTemplate +from langchain_openai import ChatOpenAI
+chain = ChatPromptTemplate.from_template("What is {input}?") | ChatOpenAI() +component = LangChainLLM(chain=chain, task="chat") +doc = component(doc) # Runs the chain on doc.data and stores output
+
healthchain/pipeline/components/integrations.py__call__(doc)
+
+Process the document using the LangChain chain. Adds outputs to .model_outputs['langchain'].
+ +healthchain/pipeline/components/integrations.py__init__(chain, task, **kwargs)
+
+Initialize with a LangChain chain.
+ +healthchain/pipeline/components/integrations.pySpacyNLP
+
+
+
+ Bases: BaseComponent[str]
A component that integrates spaCy models into the pipeline.
+This component allows using any spaCy model within the pipeline by loading +and applying it to process text documents. The spaCy doc outputs are stored +in the document's nlp annotations container under .spacy_docs.
+ + +| PARAMETER | +DESCRIPTION | +
|---|---|
+ nlp
+ |
+
+
+
+ A pre-configured spaCy Language object. +
+
+ TYPE:
+ |
+
++++++Using pre-configured pipeline
+import spacy +nlp = spacy.load("en_core_web_sm", disable=["parser"]) +component = SpacyNLP(nlp) +doc = component(doc)
+Or using model name
+component = SpacyNLP.from_model_id("en_core_web_sm", disable=["parser"]) +doc = component(doc)
+
healthchain/pipeline/components/integrations.py38 + 39 + 40 + 41 + 42 + 43 + 44 + 45 + 46 + 47 + 48 + 49 + 50 + 51 + 52 + 53 + 54 + 55 + 56 + 57 + 58 + 59 + 60 + 61 + 62 + 63 + 64 + 65 + 66 + 67 + 68 + 69 + 70 + 71 + 72 + 73 + 74 + 75 + 76 + 77 + 78 + 79 + 80 + 81 + 82 + 83 + 84 + 85 + 86 + 87 + 88 + 89 + 90 + 91 + 92 + 93 + 94 + 95 + 96 + 97 + 98 + 99 +100 +101 +102 +103 +104 +105 +106 +107 +108 +109 +110 +111 +112 +113 +114 +115 +116 +117 +118 +119 +120 +121 +122 +123 +124 +125 +126 +127 +128 +129 +130 +131 +132 +133 | |
__call__(doc)
+
+Process the document using the spaCy pipeline. Adds outputs to nlp.spacy_docs.
+ +healthchain/pipeline/components/integrations.py__init__(nlp)
+
+from_model_id(model, **kwargs)
+
+
+ classmethod
+
+
+Create a SpacyNLP component from a model identifier.
+ + +| PARAMETER | +DESCRIPTION | +
|---|---|
+ model
+ |
+
+
+
+ The name or path of the spaCy model to load. +Can be a model name like 'en_core_web_sm' or path to saved model. +
+
+ TYPE:
+ |
+
+ **kwargs
+ |
+
+
+
+ Additional configuration options passed to spacy.load. +Common options include disable, exclude, enable. +
+
+ TYPE:
+ |
+
| RETURNS | +DESCRIPTION | +
|---|---|
+ SpacyNLP
+ |
+
+
+
+ Initialized spaCy component +
+
+ TYPE:
+ |
+
| RAISES | +DESCRIPTION | +
|---|---|
+
+ ImportError
+
+ |
+
+
+
+ If spaCy or the specified model is not installed + |
+
+
+ TypeError
+
+ |
+
+
+
+ If invalid kwargs are passed to spacy.load + |
+
healthchain/pipeline/components/integrations.pyrequires_package(package_name, import_path)
+
+Decorator to check if an optional package is available.
+ + +| PARAMETER | +DESCRIPTION | +
|---|---|
+ package_name
+ |
+
+
+
+ Name of the package to install (e.g., 'langchain-core') +
+
+ TYPE:
+ |
+
+ import_path
+ |
+
+
+
+ Import path to check (e.g., 'langchain_core.runnables') +
+
+ TYPE:
+ |
+
healthchain/pipeline/components/integrations.pyTextPreProcessor
+
+
+
+ Bases: BaseComponent[Document]
A component for preprocessing text documents.
+This class applies various cleaning and tokenization steps to a Document object, +based on the provided configuration.
+ + +| ATTRIBUTE | +DESCRIPTION | +
|---|---|
tokenizer |
+
+
+
+ The tokenizer to use. Can be "basic" or a custom +tokenization function that takes a string and returns a list of tokens. Defaults to "basic". +
+
+ TYPE:
+ |
+
lowercase |
+
+
+
+ Whether to convert text to lowercase. Defaults to False. +
+
+ TYPE:
+ |
+
remove_punctuation |
+
+
+
+ Whether to remove punctuation. Defaults to False. +
+
+ TYPE:
+ |
+
standardize_spaces |
+
+
+
+ Whether to standardize spaces. Defaults to False. +
+
+ TYPE:
+ |
+
regex |
+
+
+
+ List of regex patterns and replacements. Defaults to an empty list. +
+
+ TYPE:
+ |
+
tokenizer_func |
+
+
+
+ The tokenization function. +
+
+ TYPE:
+ |
+
cleaning_steps |
+
+
+
+ List of text cleaning functions. +
+
+ TYPE:
+ |
+
healthchain/pipeline/components/preprocessors.py9 + 10 + 11 + 12 + 13 + 14 + 15 + 16 + 17 + 18 + 19 + 20 + 21 + 22 + 23 + 24 + 25 + 26 + 27 + 28 + 29 + 30 + 31 + 32 + 33 + 34 + 35 + 36 + 37 + 38 + 39 + 40 + 41 + 42 + 43 + 44 + 45 + 46 + 47 + 48 + 49 + 50 + 51 + 52 + 53 + 54 + 55 + 56 + 57 + 58 + 59 + 60 + 61 + 62 + 63 + 64 + 65 + 66 + 67 + 68 + 69 + 70 + 71 + 72 + 73 + 74 + 75 + 76 + 77 + 78 + 79 + 80 + 81 + 82 + 83 + 84 + 85 + 86 + 87 + 88 + 89 + 90 + 91 + 92 + 93 + 94 + 95 + 96 + 97 + 98 + 99 +100 +101 +102 +103 +104 +105 +106 +107 +108 +109 +110 +111 +112 +113 +114 +115 +116 +117 +118 +119 +120 +121 +122 +123 +124 +125 +126 +127 +128 +129 +130 +131 +132 +133 +134 +135 +136 +137 +138 +139 +140 +141 +142 +143 +144 +145 +146 +147 +148 +149 +150 +151 +152 +153 | |
__call__(doc)
+
+Preprocess the given Document.
+This method applies the configured cleaning steps and tokenization to the document's text (in that order).
+ + +| PARAMETER | +DESCRIPTION | +
|---|---|
+ doc
+ |
+
+
+
+ The document to preprocess. +
+
+ TYPE:
+ |
+
| RETURNS | +DESCRIPTION | +
|---|---|
+ Document
+ |
+
+
+
+ The preprocessed document with updated tokens and preprocessed text. +
+
+ TYPE:
+ |
+
healthchain/pipeline/components/preprocessors.py__init__(tokenizer='basic', lowercase=False, remove_punctuation=False, standardize_spaces=False, regex=None)
+
+Initialize the TextPreprocessor with the given configuration.
+ + +| PARAMETER | +DESCRIPTION | +
|---|---|
+ tokenizer
+ |
+
+
+
+ The tokenizer to use. Can be "basic" or a custom +tokenization function that takes a string and returns a list of tokens. Defaults to "basic". +
+
+ TYPE:
+ |
+
+ lowercase
+ |
+
+
+
+ Whether to convert text to lowercase. Defaults to False. +
+
+ TYPE:
+ |
+
+ remove_punctuation
+ |
+
+
+
+ Whether to remove punctuation. Defaults to False. +
+
+ TYPE:
+ |
+
+ standardize_spaces
+ |
+
+
+
+ Whether to standardize spaces. Defaults to False. +
+
+ TYPE:
+ |
+
+ regex
+ |
+
+
+
+ List of regex patterns and replacements. Defaults to None. +
+
+ TYPE:
+ |
+
healthchain/pipeline/components/preprocessors.pyTextPostProcessor
+
+
+
+ Bases: BaseComponent[Document]
A component for post-processing text documents, specifically for refining entities.
+This class applies post-coordination rules to entities in a Document object, +replacing entities with their refined versions based on a lookup dictionary.
+ + +| ATTRIBUTE | +DESCRIPTION | +
|---|---|
entity_lookup |
+
+
+
+ A dictionary for entity refinement lookups. +
+
+ TYPE:
+ |
+
healthchain/pipeline/components/postprocessors.py__call__(doc)
+
+Apply post-processing to the given Document.
+This method refines the entities in the document based on the entity_lookup. +If an entity exists in the lookup, it is replaced with its refined version.
+ + +| PARAMETER | +DESCRIPTION | +
|---|---|
+ doc
+ |
+
+
+
+ The document to be post-processed. +
+
+ TYPE:
+ |
+
| RETURNS | +DESCRIPTION | +
|---|---|
+ Document
+ |
+
+
+
+ The post-processed document with refined entities. +
+
+ TYPE:
+ |
+
If the entity_lookup is empty or the document has no 'entities' attribute, +the document is returned unchanged.
+healthchain/pipeline/components/postprocessors.py__init__(postcoordination_lookup=None)
+
+Initialize the TextPostProcessor with an optional postcoordination lookup.
+ + +| PARAMETER | +DESCRIPTION | +
|---|---|
+ postcoordination_lookup
+ |
+
+
+
+ A dictionary for entity refinement lookups. +If not provided, an empty dictionary will be used. +
+
+ TYPE:
+ |
+
healthchain/pipeline/components/postprocessors.pyCdsCardCreator
+
+
+
+ Bases: BaseComponent[str]
Component that creates CDS Hooks cards from model outputs or static content.
+
+This component formats text into CDS Hooks cards that can be displayed in an EHR system.
+It can create cards from either:
+1. Model-generated text stored in a document's model outputs container
+2. Static content provided during initialization
+
+The component uses Jinja2 templates to format the text into valid CDS Hooks card JSON.
+The generated cards are added to the document's CDS container.
+
+Args:
+ template (str, optional): Jinja2 template string for card creation. If not provided,
+ uses a default template that creates an info card.
+ template_path (Union[str, Path], optional): Path to a Jinja2 template file.
+ static_content (str, optional): Static text to use instead of model output.
+ source (str, optional): Source framework to get model output from (e.g. "huggingface").
+ task (str, optional): Task name to get model output from (e.g. "summarization").
+ delimiter (str, optional): String to split model output into multiple cards.
+ default_source (Dict[str, Any], optional): Default source info for cards.
+ Defaults to {"label": "Card Generated by HealthChain"}.
+
+Example:
+ >>> # Create cards from model output
+ >>> creator = CdsCardCreator(source="huggingface", task="summarization")
+ >>> doc = creator(doc) # Creates cards from model output
+ >>>
+ >>> # Create cards with static content
+ >>> creator = CdsCardCreator(static_content="Static card message")
+ >>> doc = creator(doc) # Creates card with static content
+ >>>
+ >>> # Create cards with custom template
+ >>> template = '''
+ ... {
+ ... "summary": "{{ model_output[:140] }}",
+ ... "indicator": "info",
+ ... "source": {{ default_source | tojson }},
+ ... "detail": "{{ model_output }}"
+ ... }
+ ... '''
+ >>> creator = CdsCardCreator(
+ ... template=template,
+ ... source="langchain",
+ ... task="chat",
+ ... delimiter="
+" + ... ) + >>> doc = creator(doc) # Creates cards split by newlines
+ + + + + + +healthchain/pipeline/components/cdscardcreator.py15 + 16 + 17 + 18 + 19 + 20 + 21 + 22 + 23 + 24 + 25 + 26 + 27 + 28 + 29 + 30 + 31 + 32 + 33 + 34 + 35 + 36 + 37 + 38 + 39 + 40 + 41 + 42 + 43 + 44 + 45 + 46 + 47 + 48 + 49 + 50 + 51 + 52 + 53 + 54 + 55 + 56 + 57 + 58 + 59 + 60 + 61 + 62 + 63 + 64 + 65 + 66 + 67 + 68 + 69 + 70 + 71 + 72 + 73 + 74 + 75 + 76 + 77 + 78 + 79 + 80 + 81 + 82 + 83 + 84 + 85 + 86 + 87 + 88 + 89 + 90 + 91 + 92 + 93 + 94 + 95 + 96 + 97 + 98 + 99 +100 +101 +102 +103 +104 +105 +106 +107 +108 +109 +110 +111 +112 +113 +114 +115 +116 +117 +118 +119 +120 +121 +122 +123 +124 +125 +126 +127 +128 +129 +130 +131 +132 +133 +134 +135 +136 +137 +138 +139 +140 +141 +142 +143 +144 +145 +146 +147 +148 +149 +150 +151 +152 +153 +154 +155 +156 +157 +158 +159 +160 +161 +162 +163 +164 +165 +166 +167 +168 +169 +170 +171 +172 +173 +174 +175 +176 +177 +178 +179 +180 +181 +182 +183 +184 +185 +186 +187 +188 +189 +190 +191 | |
__call__(doc)
+
+Process a document and create CDS Hooks cards from model outputs or static content.
+Creates cards in one of two ways: +1. From model-generated text stored in the document's model outputs container, + accessed using the configured source and task +2. From static content provided during initialization
+The generated text can optionally be split into multiple cards using a delimiter. +Each piece of text is formatted using the configured template into a CDS Hooks card +and added to the document's CDS container.
+ + +| PARAMETER | +DESCRIPTION | +
|---|---|
+ doc
+ |
+
+
+
+ Document containing model outputs and CDS container +
+
+ TYPE:
+ |
+
| RETURNS | +DESCRIPTION | +
|---|---|
+ Document
+ |
+
+
+
+ The input document with generated CDS cards added to its CDS container +
+
+ TYPE:
+ |
+
| RAISES | +DESCRIPTION | +
|---|---|
+
+ ValueError
+
+ |
+
+
+
+ If neither model configuration (source and task) nor static content +is provided for card creation + |
+
healthchain/pipeline/components/cdscardcreator.pycreate_card(content)
+
+Creates a CDS Card using the template and model output.
+ +healthchain/pipeline/components/cdscardcreator.pyBaseConnector
+
+
+
+ Bases: Generic[T], ABC
Abstract base class for all connectors in the pipeline.
+This class should be subclassed to create specific connectors. +Subclasses must implement the input and output methods.
+ + + + + + +healthchain/io/base.pyinput(data)
+
+
+ abstractmethod
+
+
+Convert input data to the pipeline's internal format.
+ + +| PARAMETER | +DESCRIPTION | +
|---|---|
+ data
+ |
+
+
+
+ The input data to be converted. +
+
+ TYPE:
+ |
+
| RETURNS | +DESCRIPTION | +
|---|---|
+
+ DataContainer[T]
+
+ |
+
+
+
+ DataContainer[T]: The converted data. + |
+
healthchain/io/base.pyoutput(data)
+
+
+ abstractmethod
+
+
+Convert pipeline's internal format to output data.
+ + +| PARAMETER | +DESCRIPTION | +
|---|---|
+ data
+ |
+
+
+
+ The data to be converted for output. +
+
+ TYPE:
+ |
+
| RETURNS | +DESCRIPTION | +
|---|---|
+
+ DataContainer[T]
+
+ |
+
+
+
+ DataContainer[T]: The converted output data. + |
+
healthchain/io/base.pyCdaConnector
+
+
+
+ Bases: BaseConnector
CDAConnector class for handling CDA (Clinical Document Architecture) documents.
+This connector is responsible for parsing CDA documents, extracting relevant +clinical data, and updating the document with new information. It serves as +both an input and output connector in the pipeline.
+ + +| ATTRIBUTE | +DESCRIPTION | +
|---|---|
overwrite |
+
+
+
+ Flag to determine if existing data should be overwritten + when updating the CDA document. +
+
+ TYPE:
+ |
+
cda_doc |
+
+
+
+ The parsed CDA document. +
+
+ TYPE:
+ |
+
| METHOD | +DESCRIPTION | +
|---|---|
input |
+
+
+
+ Parses the input CDA document and extracts clinical data. + |
+
output |
+
+
+
+ Updates the CDA document with new data and returns the response. + |
+
healthchain/io/cdaconnector.py13 + 14 + 15 + 16 + 17 + 18 + 19 + 20 + 21 + 22 + 23 + 24 + 25 + 26 + 27 + 28 + 29 + 30 + 31 + 32 + 33 + 34 + 35 + 36 + 37 + 38 + 39 + 40 + 41 + 42 + 43 + 44 + 45 + 46 + 47 + 48 + 49 + 50 + 51 + 52 + 53 + 54 + 55 + 56 + 57 + 58 + 59 + 60 + 61 + 62 + 63 + 64 + 65 + 66 + 67 + 68 + 69 + 70 + 71 + 72 + 73 + 74 + 75 + 76 + 77 + 78 + 79 + 80 + 81 + 82 + 83 + 84 + 85 + 86 + 87 + 88 + 89 + 90 + 91 + 92 + 93 + 94 + 95 + 96 + 97 + 98 + 99 +100 +101 +102 +103 +104 +105 +106 +107 +108 +109 +110 +111 +112 +113 +114 +115 +116 +117 +118 +119 +120 +121 +122 +123 +124 +125 +126 +127 +128 | |
input(in_data)
+
+Parse the input CDA document and extract clinical data.
+This method takes a CdaRequest object containing the CDA document as input, +parses it using the CdaAnnotator, and extracts relevant clinical data. +The extracted data is then used to create a CcdData object and a healthchain +Document object, which is returned.
+ + +| PARAMETER | +DESCRIPTION | +
|---|---|
+ in_data
+ |
+
+
+
+ The input request containing the CDA document. +
+
+ TYPE:
+ |
+
| RETURNS | +DESCRIPTION | +
|---|---|
+ Document
+ |
+
+
+
+ A Document object containing the extracted clinical data + and the original note text. +
+
+ TYPE:
+ |
+
healthchain/io/cdaconnector.pyoutput(out_data)
+
+Update the CDA document with new data and return the response.
+This method takes a Document object containing updated clinical data, +updates the CDA document with this new information, and returns a +CdaResponse object with the updated CDA document.
+ + +| PARAMETER | +DESCRIPTION | +
|---|---|
+ out_data
+ |
+
+
+
+ A Document object containing the updated + clinical data (problems, allergies, medications). +
+
+ TYPE:
+ |
+
| RETURNS | +DESCRIPTION | +
|---|---|
+ CdaResponse
+ |
+
+
+
+ A response object containing the updated CDA document. +
+
+ TYPE:
+ |
+
The method updates the CDA document with new problems, allergies,
+and medications if they are present in the input Document object.
+The update behavior (overwrite or append) is determined by the
+overwrite attribute of the CdaConnector instance.
healthchain/io/cdaconnector.pyCdsFhirConnector
+
+
+
+ Bases: BaseConnector
CdsFhirConnector class for handling FHIR (Fast Healthcare Interoperability Resources) documents +for CDS Hooks.
+This connector facilitates the conversion between CDSRequest objects and Document objects, +as well as the creation of CDSResponse objects from processed Documents.
+ + +| ATTRIBUTE | +DESCRIPTION | +
|---|---|
hook_name |
+
+
+
+ The name of the CDS Hook being used. +
+
+ TYPE:
+ |
+
healthchain/io/cdsfhirconnector.py13 + 14 + 15 + 16 + 17 + 18 + 19 + 20 + 21 + 22 + 23 + 24 + 25 + 26 + 27 + 28 + 29 + 30 + 31 + 32 + 33 + 34 + 35 + 36 + 37 + 38 + 39 + 40 + 41 + 42 + 43 + 44 + 45 + 46 + 47 + 48 + 49 + 50 + 51 + 52 + 53 + 54 + 55 + 56 + 57 + 58 + 59 + 60 + 61 + 62 + 63 + 64 + 65 + 66 + 67 + 68 + 69 + 70 + 71 + 72 + 73 + 74 + 75 + 76 + 77 + 78 + 79 + 80 + 81 + 82 + 83 + 84 + 85 + 86 + 87 + 88 + 89 + 90 + 91 + 92 + 93 + 94 + 95 + 96 + 97 + 98 + 99 +100 +101 +102 +103 +104 +105 +106 +107 | |
input(in_data)
+
+Converts a CDSRequest object into a Document object containing FHIR resources.
+This method takes a CDSRequest object as input, extracts the context and prefetch data, +and creates a CdsFhirData object. It then returns a Document object containing the FHIR data +and any extracted text content from DocumentReference resources.
+ + +| PARAMETER | +DESCRIPTION | +
|---|---|
+ in_data
+ |
+
+
+
+ The input CDSRequest object containing context and prefetch data. +
+
+ TYPE:
+ |
+
| RETURNS | +DESCRIPTION | +
|---|---|
+ Document
+ |
+
+
+
+ A Document object with the following attributes: +- data: Either a string representation of the prefetch data, or if a DocumentReference + is present, the text content from that resource +- hl7: Contains the CdsFhirData object with context and prefetch data +
+
+ TYPE:
+ |
+
| RAISES | +DESCRIPTION | +
|---|---|
+
+ ValueError
+
+ |
+
+
+
+ If neither prefetch nor fhirServer is provided in the input data + |
+
+
+ NotImplementedError
+
+ |
+
+
+
+ If fhirServer is provided (not yet implemented) + |
+
+
+ ValueError
+
+ |
+
+
+
+ If the provided prefetch data is invalid + |
+
The method currently only supports prefetch data and does not handle FHIR server interactions. +When a DocumentReference resource is present in the prefetch data, its text content will be +extracted and stored as the Document's data field.
+healthchain/io/cdsfhirconnector.pyoutput(out_data)
+
+Generates a CDSResponse object from a processed Document object.
+This method takes a Document object that has been processed and potentially +contains CDS cards and system actions. It creates and returns a CDSResponse +object based on the contents of the Document.
+ + +| PARAMETER | +DESCRIPTION | +
|---|---|
+ out_data
+ |
+
+
+
+ A Document object potentially containing CDS cards + and system actions. +
+
+ TYPE:
+ |
+
| RETURNS | +DESCRIPTION | +
|---|---|
+ CDSResponse
+ |
+
+
+
+ A response object containing CDS cards and optional system actions. + If no cards are found in the Document, an empty list of cards is returned. +
+
+ TYPE:
+ |
+
healthchain/io/cdsfhirconnector.pyBaseDocument
+
+
+
+ dataclass
+
+
+
+ Bases: DataContainer[str]
Base document container for raw text content.
+ + + + + + +healthchain/io/containers/base.pyDataContainer
+
+
+
+ dataclass
+
+
+
+ Bases: Generic[T]
A generic container for data.
+This class represents a container for data with a specific type T.
+ + +| ATTRIBUTE | +DESCRIPTION | +
|---|---|
data |
+
+
+
+ The data stored in the container. +
+
+ TYPE:
+ |
+
| METHOD | +DESCRIPTION | +
|---|---|
to_dict |
+
+
+
+ Converts the container's data to a dictionary. + |
+
to_json |
+
+
+
+ Converts the container's data to a JSON string. + |
+
from_dict |
+
+
+
+ Dict[str, Any]) -> "DataContainer": +Creates a DataContainer instance from a dictionary. + |
+
from_json |
+
+
+
+ str) -> "DataContainer": +Creates a DataContainer instance from a JSON string. + |
+
healthchain/io/containers/base.pyDocument
+
+
+
+ dataclass
+
+
+
+ Bases: BaseDocument
A document container that extends BaseDocument with rich annotation capabilities.
+This class extends BaseDocument to handle textual document data and annotations from +various sources. It serves as the main data structure passed through processing pipelines, +accumulating annotations and analysis results at each step.
+The Document class provides a comprehensive representation that can include: +- Raw text and basic tokenization +- NLP annotations (tokens, entities, embeddings, spaCy docs) +- Clinical concepts (problems, medications, allergies) +- Structured clinical documents (CCD, FHIR) +- Clinical decision support results (cards, actions) +- ML model outputs (Hugging Face, LangChain)
+ + +| ATTRIBUTE | +DESCRIPTION | +
|---|---|
nlp |
+
+
+
+ Container for NLP-related annotations like tokens and entities +
+
+ TYPE:
+ |
+
concepts |
+
+
+
+ Container for extracted medical concepts +
+
+ TYPE:
+ |
+
hl7 |
+
+
+
+ Container for structured clinical documents (CCD, FHIR) +
+
+ TYPE:
+ |
+
cds |
+
+
+
+ Container for clinical decision support results +
+
+ TYPE:
+ |
+
models |
+
+
+
+ Container for ML model outputs +
+
+ TYPE:
+ |
+
The class provides methods to: +- Add and access medical concepts +- Generate structured clinical documents +- Get basic text statistics +- Iterate over tokens +- Access raw text
+ + +BaseDocument: Provides base document functionality and raw text storage
+healthchain/io/containers/document.py308 +309 +310 +311 +312 +313 +314 +315 +316 +317 +318 +319 +320 +321 +322 +323 +324 +325 +326 +327 +328 +329 +330 +331 +332 +333 +334 +335 +336 +337 +338 +339 +340 +341 +342 +343 +344 +345 +346 +347 +348 +349 +350 +351 +352 +353 +354 +355 +356 +357 +358 +359 +360 +361 +362 +363 +364 +365 +366 +367 +368 +369 +370 +371 +372 +373 +374 +375 +376 +377 +378 +379 +380 +381 +382 +383 +384 +385 +386 +387 +388 +389 +390 +391 +392 +393 +394 +395 +396 +397 +398 +399 +400 +401 +402 +403 +404 +405 +406 +407 +408 +409 +410 +411 +412 +413 +414 +415 +416 +417 +418 +419 +420 +421 +422 +423 +424 +425 +426 +427 +428 +429 +430 +431 +432 +433 +434 +435 +436 +437 +438 +439 +440 +441 +442 +443 +444 +445 +446 +447 +448 +449 +450 +451 +452 +453 +454 +455 +456 +457 +458 +459 +460 +461 +462 +463 +464 +465 +466 +467 +468 +469 +470 +471 +472 +473 | |
__post_init__()
+
+Initialize the document with basic tokenization if needed.
+ +healthchain/io/containers/document.pyadd_concepts(problems=None, medications=None, allergies=None)
+
+Add extracted medical concepts to the document.
+This method adds medical concepts (problems, medications, allergies) to their +respective lists in the document's concepts container. Each concept type is +optional and will only be added if provided.
+ + +| PARAMETER | +DESCRIPTION | +
|---|---|
+ problems
+ |
+
+
+
+ List of medical problems/conditions +to add to the document. Defaults to None. +
+
+ TYPE:
+ |
+
+ medications
+ |
+
+
+
+ List of medications +to add to the document. Defaults to None. +
+
+ TYPE:
+ |
+
+ allergies
+ |
+
+
+
+ List of allergies +to add to the document. Defaults to None. +
+
+ TYPE:
+ |
+
++++++doc.add_concepts( +... problems=[ProblemConcept(display_name="Hypertension")], +... medications=[MedicationConcept(display_name="Aspirin")] +... )
+
healthchain/io/containers/document.pygenerate_ccd(overwrite=False)
+
+Generate a CCD (Continuity of Care Document) from the current medical concepts.
+This method creates or updates a CCD in the hl7 container using the +medical concepts (problems, medications, allergies) currently stored in the document. +The CCD is a standard format for exchanging clinical information.
+ + +| PARAMETER | +DESCRIPTION | +
|---|---|
+ overwrite
+ |
+
+
+
+ If True, overwrites any existing CCD data. +If False, merges with existing CCD data. Defaults to False. +
+
+ TYPE:
+ |
+
| RETURNS | +DESCRIPTION | +
|---|---|
+ CcdData
+ |
+
+
+
+ The generated CCD data. +
+
+ TYPE:
+ |
+
++++++doc.add_concepts(problems=[ProblemConcept(display_name="Hypertension")]) +doc.generate_ccd() # Creates CCD with the hypertension problem
+
healthchain/io/containers/document.pyTabular
+
+
+
+ dataclass
+
+
+
+ Bases: DataContainer[DataFrame]
A container for tabular data, wrapping a pandas DataFrame.
+ + +| ATTRIBUTE | +DESCRIPTION | +
|---|---|
data |
+
+
+
+ The pandas DataFrame containing the tabular data. +
+
+ TYPE:
+ |
+
| METHOD | +DESCRIPTION | +
|---|---|
__post_init__ |
+
+
+
+ Validates that the data is a pandas DataFrame. + |
+
columns |
+
+
+
+ Property that returns a list of column names. + |
+
index |
+
+
+
+ Property that returns the DataFrame's index. + |
+
dtypes |
+
+
+
+ Property that returns a dictionary of column names and their data types. + |
+
column_count |
+
+
+
+ Returns the number of columns in the DataFrame. + |
+
row_count |
+
+
+
+ Returns the number of rows in the DataFrame. + |
+
get_dtype |
+
+
+
+ str): Returns the data type of a specific column. + |
+
__iter__ |
+
+
+
+ Returns an iterator over the column names. + |
+
__len__ |
+
+
+
+ Returns the number of rows in the DataFrame. + |
+
describe |
+
+
+
+ Returns a string description of the tabular data. + |
+
remove_column |
+
+
+
+ str): Removes a column from the DataFrame. + |
+
from_csv |
+
+
+
+ str, **kwargs): Class method to create a Tabular object from a CSV file. + |
+
from_dict |
+
+
+
+ Dict[str, Any]): Class method to create a Tabular object from a dictionary. + |
+
to_csv |
+
+
+
+ str, **kwargs): Saves the DataFrame to a CSV file. + |
+
healthchain/io/containers/tabular.pybase
+
+
+BaseDocument
+
+
+
+ dataclass
+
+
+
+ Bases: DataContainer[str]
Base document container for raw text content.
+ + + + + + +healthchain/io/containers/base.pyDataContainer
+
+
+
+ dataclass
+
+
+
+ Bases: Generic[T]
A generic container for data.
+This class represents a container for data with a specific type T.
+ + +| ATTRIBUTE | +DESCRIPTION | +
|---|---|
data |
+
+
+
+ The data stored in the container. +
+
+ TYPE:
+ |
+
| METHOD | +DESCRIPTION | +
|---|---|
to_dict |
+
+
+
+ Converts the container's data to a dictionary. + |
+
to_json |
+
+
+
+ Converts the container's data to a JSON string. + |
+
from_dict |
+
+
+
+ Dict[str, Any]) -> "DataContainer": +Creates a DataContainer instance from a dictionary. + |
+
from_json |
+
+
+
+ str) -> "DataContainer": +Creates a DataContainer instance from a JSON string. + |
+
healthchain/io/containers/base.pydocument
+
+
+CdsAnnotations
+
+
+
+ dataclass
+
+
+Container for Clinical Decision Support (CDS) results.
+This class stores the outputs from clinical decision support systems, +including CDS Hooks cards and suggested clinical actions. The cards contain +recommendations, warnings, and other decision support content that can be +displayed to clinicians. Actions represent specific clinical tasks or +interventions that are suggested based on the analysis.
+ + +| ATTRIBUTE | +DESCRIPTION | +
|---|---|
_cards |
+
+
+
+ Internal storage for CDS Hooks cards containing +clinical recommendations, warnings, or other decision support content. +
+
+ TYPE:
+ |
+
_actions |
+
+
+
+ Internal storage for suggested clinical actions +that could be taken based on the CDS analysis. +
+
+ TYPE:
+ |
+
| METHOD | +DESCRIPTION | +
|---|---|
set_cards |
+
+
+
+ List[Card]): Sets the list of CDS Hooks cards + |
+
get_cards |
+
+
+
+ Returns the current list of cards if any exist + |
+
set_actions |
+
+
+
+ List[Action]): Sets the list of suggested clinical actions + |
+
get_actions |
+
+
+
+ Returns the current list of actions if any exist + |
+
healthchain/io/containers/document.pyDocument
+
+
+
+ dataclass
+
+
+
+ Bases: BaseDocument
A document container that extends BaseDocument with rich annotation capabilities.
+This class extends BaseDocument to handle textual document data and annotations from +various sources. It serves as the main data structure passed through processing pipelines, +accumulating annotations and analysis results at each step.
+The Document class provides a comprehensive representation that can include: +- Raw text and basic tokenization +- NLP annotations (tokens, entities, embeddings, spaCy docs) +- Clinical concepts (problems, medications, allergies) +- Structured clinical documents (CCD, FHIR) +- Clinical decision support results (cards, actions) +- ML model outputs (Hugging Face, LangChain)
+ + +| ATTRIBUTE | +DESCRIPTION | +
|---|---|
nlp |
+
+
+
+ Container for NLP-related annotations like tokens and entities +
+
+ TYPE:
+ |
+
concepts |
+
+
+
+ Container for extracted medical concepts +
+
+ TYPE:
+ |
+
hl7 |
+
+
+
+ Container for structured clinical documents (CCD, FHIR) +
+
+ TYPE:
+ |
+
cds |
+
+
+
+ Container for clinical decision support results +
+
+ TYPE:
+ |
+
models |
+
+
+
+ Container for ML model outputs +
+
+ TYPE:
+ |
+
The class provides methods to: +- Add and access medical concepts +- Generate structured clinical documents +- Get basic text statistics +- Iterate over tokens +- Access raw text
+ + +BaseDocument: Provides base document functionality and raw text storage
+healthchain/io/containers/document.py308 +309 +310 +311 +312 +313 +314 +315 +316 +317 +318 +319 +320 +321 +322 +323 +324 +325 +326 +327 +328 +329 +330 +331 +332 +333 +334 +335 +336 +337 +338 +339 +340 +341 +342 +343 +344 +345 +346 +347 +348 +349 +350 +351 +352 +353 +354 +355 +356 +357 +358 +359 +360 +361 +362 +363 +364 +365 +366 +367 +368 +369 +370 +371 +372 +373 +374 +375 +376 +377 +378 +379 +380 +381 +382 +383 +384 +385 +386 +387 +388 +389 +390 +391 +392 +393 +394 +395 +396 +397 +398 +399 +400 +401 +402 +403 +404 +405 +406 +407 +408 +409 +410 +411 +412 +413 +414 +415 +416 +417 +418 +419 +420 +421 +422 +423 +424 +425 +426 +427 +428 +429 +430 +431 +432 +433 +434 +435 +436 +437 +438 +439 +440 +441 +442 +443 +444 +445 +446 +447 +448 +449 +450 +451 +452 +453 +454 +455 +456 +457 +458 +459 +460 +461 +462 +463 +464 +465 +466 +467 +468 +469 +470 +471 +472 +473 | |
__post_init__()
+
+Initialize the document with basic tokenization if needed.
+ +healthchain/io/containers/document.pyadd_concepts(problems=None, medications=None, allergies=None)
+
+Add extracted medical concepts to the document.
+This method adds medical concepts (problems, medications, allergies) to their +respective lists in the document's concepts container. Each concept type is +optional and will only be added if provided.
+ + +| PARAMETER | +DESCRIPTION | +
|---|---|
+ problems
+ |
+
+
+
+ List of medical problems/conditions +to add to the document. Defaults to None. +
+
+ TYPE:
+ |
+
+ medications
+ |
+
+
+
+ List of medications +to add to the document. Defaults to None. +
+
+ TYPE:
+ |
+
+ allergies
+ |
+
+
+
+ List of allergies +to add to the document. Defaults to None. +
+
+ TYPE:
+ |
+
++++++doc.add_concepts( +... problems=[ProblemConcept(display_name="Hypertension")], +... medications=[MedicationConcept(display_name="Aspirin")] +... )
+
healthchain/io/containers/document.pygenerate_ccd(overwrite=False)
+
+Generate a CCD (Continuity of Care Document) from the current medical concepts.
+This method creates or updates a CCD in the hl7 container using the +medical concepts (problems, medications, allergies) currently stored in the document. +The CCD is a standard format for exchanging clinical information.
+ + +| PARAMETER | +DESCRIPTION | +
|---|---|
+ overwrite
+ |
+
+
+
+ If True, overwrites any existing CCD data. +If False, merges with existing CCD data. Defaults to False. +
+
+ TYPE:
+ |
+
| RETURNS | +DESCRIPTION | +
|---|---|
+ CcdData
+ |
+
+
+
+ The generated CCD data. +
+
+ TYPE:
+ |
+
++++++doc.add_concepts(problems=[ProblemConcept(display_name="Hypertension")]) +doc.generate_ccd() # Creates CCD with the hypertension problem
+
healthchain/io/containers/document.pyHL7Data
+
+
+
+ dataclass
+
+
+Container for structured clinical document formats.
+This class stores and manages structured clinical data in different formats, +including CCD (Continuity of Care Document) and FHIR (Fast Healthcare +Interoperability Resources).
+ + +| ATTRIBUTE | +DESCRIPTION | +
|---|---|
_ccd_data |
+
+
+
+ Clinical data in CCD format, containing +problems, medications, allergies and other clinical information. +
+
+ TYPE:
+ |
+
_fhir_data |
+
+
+
+ Clinical data in FHIR format for +clinical decision support. +
+
+ TYPE:
+ |
+
| METHOD | +DESCRIPTION | +
|---|---|
update_ccd_from_concepts |
+
+
+
+ ConceptLists, overwrite: bool = False) -> CcdData: +Updates the CCD data with new clinical concepts, either by overwriting or extending +existing data. + |
+
get_fhir_data |
+
+
+
+ Returns the FHIR format clinical data if it exists, otherwise None. + |
+
get_ccd_data |
+
+
+
+ Returns the CCD format clinical data if it exists, otherwise None. + |
+
healthchain/io/containers/document.py194 +195 +196 +197 +198 +199 +200 +201 +202 +203 +204 +205 +206 +207 +208 +209 +210 +211 +212 +213 +214 +215 +216 +217 +218 +219 +220 +221 +222 +223 +224 +225 +226 +227 +228 +229 +230 +231 +232 +233 +234 +235 +236 +237 +238 +239 +240 +241 +242 +243 +244 +245 +246 +247 +248 +249 +250 +251 +252 +253 +254 +255 +256 +257 +258 +259 +260 +261 +262 +263 +264 +265 | |
update_ccd_from_concepts(concepts, overwrite=False)
+
+Updates the CCD data with new clinical concepts.
+This method takes a ConceptLists object containing problems, medications, +and allergies, and updates the internal CCD data accordingly. If no CCD +data exists, it creates a new CcdData instance.
+ + +| PARAMETER | +DESCRIPTION | +
|---|---|
+ concepts
+ |
+
+
+
+ The new clinical concepts to add, containing +problems, medications, and allergies lists. +
+
+ TYPE:
+ |
+
+ overwrite
+ |
+
+
+
+ If True, replaces existing concepts. +If False, extends the existing lists. Defaults to False. +
+
+ TYPE:
+ |
+
| RETURNS | +DESCRIPTION | +
|---|---|
+ CcdData
+ |
+
+
+
+ The updated CCD data. +
+
+ TYPE:
+ |
+
healthchain/io/containers/document.pyModelOutputs
+
+
+
+ dataclass
+
+
+Container for storing and managing third-party integration model outputs.
+This class stores outputs from different NLP/ML frameworks like Hugging Face +and LangChain, organizing them by task type. It also maintains a list of +generated text outputs across frameworks.
+ + +| ATTRIBUTE | +DESCRIPTION | +
|---|---|
_huggingface_results |
+
+
+
+ Dictionary storing Hugging Face model +outputs, keyed by task name. +
+
+ TYPE:
+ |
+
_langchain_results |
+
+
+
+ Dictionary storing LangChain outputs, +keyed by task name. +
+
+ TYPE:
+ |
+
| METHOD | +DESCRIPTION | +
|---|---|
add_output |
+
+
+
+ str, task: str, output: Any): Adds a model output for a +specific source and task. For text generation tasks, also extracts and +stores the generated text. + |
+
get_output |
+
+
+
+ str, task: str, default: Any = None) -> Any: Gets the model +output for a specific source and task. Returns default if not found. + |
+
get_generated_text |
+
+
+
+ Returns the list of generated text outputs + |
+
healthchain/io/containers/document.py86 + 87 + 88 + 89 + 90 + 91 + 92 + 93 + 94 + 95 + 96 + 97 + 98 + 99 +100 +101 +102 +103 +104 +105 +106 +107 +108 +109 +110 +111 +112 +113 +114 +115 +116 +117 +118 +119 +120 +121 +122 +123 +124 +125 +126 +127 +128 +129 +130 +131 +132 +133 +134 +135 +136 +137 +138 +139 +140 +141 +142 +143 +144 +145 +146 +147 +148 +149 +150 +151 +152 +153 +154 +155 +156 +157 +158 +159 +160 +161 +162 +163 +164 +165 +166 +167 +168 +169 +170 +171 +172 +173 +174 +175 +176 +177 +178 +179 +180 +181 +182 +183 +184 +185 +186 +187 +188 +189 +190 +191 | |
get_generated_text(source, task)
+
+Returns generated text outputs for a given source and task.
+Handles different output formats for Hugging Face and LangChain. For +Hugging Face, it extracts the last message content from chat-style +outputs and common keys like "generated_text", "summary_text", and +"translation". For LangChain, it converts JSON outputs to strings, and returns +the output as is if it is already a string.
+ + +| PARAMETER | +DESCRIPTION | +
|---|---|
+ source
+ |
+
+
+
+ Framework name (e.g., "huggingface", "langchain"). +
+
+ TYPE:
+ |
+
+ task
+ |
+
+
+
+ Task name for retrieving generated text. +
+
+ TYPE:
+ |
+
| RETURNS | +DESCRIPTION | +
|---|---|
+
+ List[str]
+
+ |
+
+
+
+ List[str]: List of generated text outputs, or an empty list if none. + |
+
healthchain/io/containers/document.py128 +129 +130 +131 +132 +133 +134 +135 +136 +137 +138 +139 +140 +141 +142 +143 +144 +145 +146 +147 +148 +149 +150 +151 +152 +153 +154 +155 +156 +157 +158 +159 +160 +161 +162 +163 +164 +165 +166 +167 +168 +169 +170 +171 +172 +173 +174 +175 +176 +177 +178 +179 +180 +181 +182 +183 +184 +185 +186 +187 +188 +189 +190 +191 | |
NlpAnnotations
+
+
+
+ dataclass
+
+
+Container for NLP-specific annotations and results.
+This class stores various NLP annotations and processing results from text analysis, +including preprocessed text, tokens, named entities, embeddings and spaCy documents.
+ + +| ATTRIBUTE | +DESCRIPTION | +
|---|---|
_preprocessed_text |
+
+
+
+ The preprocessed version of the input text. +
+
+ TYPE:
+ |
+
_tokens |
+
+
+
+ List of tokenized words from the text. +
+
+ TYPE:
+ |
+
_entities |
+
+
+
+ Named entities extracted from the text, with their labels and positions. +
+
+ TYPE:
+ |
+
_embeddings |
+
+
+
+ Vector embeddings generated from the text. +
+
+ TYPE:
+ |
+
_spacy_doc |
+
+
+
+ The processed spaCy Doc object. +
+
+ TYPE:
+ |
+
| METHOD | +DESCRIPTION | +
|---|---|
add_spacy_doc |
+
+
+
+ SpacyDoc): Processes a spaCy Doc to extract tokens and entities. + |
+
get_spacy_doc |
+
+
+
+ Returns the stored spaCy Doc object. + |
+
get_tokens |
+
+
+
+ Returns the list of tokens. + |
+
set_tokens |
+
+
+
+ List[str]): Sets the token list. + |
+
set_entities |
+
+
+
+ List[Dict[str, Any]]): Sets the named entities list. + |
+
get_entities |
+
+
+
+ Returns the list of named entities. + |
+
get_embeddings |
+
+
+
+ Returns the vector embeddings. + |
+
set_embeddings |
+
+
+
+ List[float]): Sets the vector embeddings. + |
+
healthchain/io/containers/document.pytabular
+
+
+Tabular
+
+
+
+ dataclass
+
+
+
+ Bases: DataContainer[DataFrame]
A container for tabular data, wrapping a pandas DataFrame.
+ + +| ATTRIBUTE | +DESCRIPTION | +
|---|---|
data |
+
+
+
+ The pandas DataFrame containing the tabular data. +
+
+ TYPE:
+ |
+
| METHOD | +DESCRIPTION | +
|---|---|
__post_init__ |
+
+
+
+ Validates that the data is a pandas DataFrame. + |
+
columns |
+
+
+
+ Property that returns a list of column names. + |
+
index |
+
+
+
+ Property that returns the DataFrame's index. + |
+
dtypes |
+
+
+
+ Property that returns a dictionary of column names and their data types. + |
+
column_count |
+
+
+
+ Returns the number of columns in the DataFrame. + |
+
row_count |
+
+
+
+ Returns the number of rows in the DataFrame. + |
+
get_dtype |
+
+
+
+ str): Returns the data type of a specific column. + |
+
__iter__ |
+
+
+
+ Returns an iterator over the column names. + |
+
__len__ |
+
+
+
+ Returns the number of rows in the DataFrame. + |
+
describe |
+
+
+
+ Returns a string description of the tabular data. + |
+
remove_column |
+
+
+
+ str): Removes a column from the DataFrame. + |
+
from_csv |
+
+
+
+ str, **kwargs): Class method to create a Tabular object from a CSV file. + |
+
from_dict |
+
+
+
+ Dict[str, Any]): Class method to create a Tabular object from a dictionary. + |
+
to_csv |
+
+
+
+ str, **kwargs): Saves the DataFrame to a CSV file. + |
+
healthchain/io/containers/tabular.pyCdsDataGenerator
+
+
+A class to generate CDS (Clinical Decision Support) data based on specified workflows and constraints.
+ + +| ATTRIBUTE | +DESCRIPTION | +
|---|---|
registry |
+
+
+
+ A registry of data generators. +
+
+ TYPE:
+ |
+
mappings |
+
+
+
+ A mapping of workflows to their respective data generators. +
+
+ TYPE:
+ |
+
data |
+
+
+
+ The generated CDS FHIR data. +
+
+ TYPE:
+ |
+
healthchain/data_generators/cdsdatagenerator.py19 + 20 + 21 + 22 + 23 + 24 + 25 + 26 + 27 + 28 + 29 + 30 + 31 + 32 + 33 + 34 + 35 + 36 + 37 + 38 + 39 + 40 + 41 + 42 + 43 + 44 + 45 + 46 + 47 + 48 + 49 + 50 + 51 + 52 + 53 + 54 + 55 + 56 + 57 + 58 + 59 + 60 + 61 + 62 + 63 + 64 + 65 + 66 + 67 + 68 + 69 + 70 + 71 + 72 + 73 + 74 + 75 + 76 + 77 + 78 + 79 + 80 + 81 + 82 + 83 + 84 + 85 + 86 + 87 + 88 + 89 + 90 + 91 + 92 + 93 + 94 + 95 + 96 + 97 + 98 + 99 +100 +101 +102 +103 +104 +105 +106 +107 +108 +109 +110 +111 +112 +113 +114 +115 +116 +117 +118 +119 +120 +121 +122 +123 +124 +125 +126 +127 +128 +129 +130 +131 +132 +133 +134 +135 +136 +137 +138 +139 +140 +141 +142 +143 +144 +145 +146 +147 +148 +149 +150 +151 +152 +153 +154 +155 +156 +157 +158 +159 +160 +161 | |
fetch_generator(generator_name)
+
+Fetches a data generator function by its name from the registry.
+ + +| PARAMETER | +DESCRIPTION | +
|---|---|
+ generator_name
+ |
+
+
+
+ The name of the data generator to fetch. +
+
+ TYPE:
+ |
+
| RETURNS | +DESCRIPTION | +
|---|---|
+ Callable
+ |
+
+
+
+ The data generator function. +
+
+ TYPE:
+ |
+
healthchain/data_generators/cdsdatagenerator.pyfree_text_parser(path_to_csv, column_name)
+
+Parses free text data from a CSV file and converts it into a list of DocumentReference models.
+ + +| PARAMETER | +DESCRIPTION | +
|---|---|
+ path_to_csv
+ |
+
+
+
+ The path to the CSV file containing free text data. +
+
+ TYPE:
+ |
+
+ column_name
+ |
+
+
+
+ The column name in the CSV file to use for free text data. +
+
+ TYPE:
+ |
+
| RETURNS | +DESCRIPTION | +
|---|---|
+ dict
+ |
+
+
+
+ A dictionary of parsed free text data converted into DocumentReference models. +
+
+ TYPE:
+ |
+
healthchain/data_generators/cdsdatagenerator.pygenerate(constraints=None, free_text_path=None, column_name=None, random_seed=None)
+
+Generates CDS data based on the current workflow, constraints, and optional free text data.
+ + +| PARAMETER | +DESCRIPTION | +
|---|---|
+ constraints
+ |
+
+
+
+ A list of constraints to apply to the data generation. +
+
+ TYPE:
+ |
+
+ free_text_path
+ |
+
+
+
+ The path to a CSV file containing free text data. +
+
+ TYPE:
+ |
+
+ column_name
+ |
+
+
+
+ The column name in the CSV file to use for free text data. +
+
+ TYPE:
+ |
+
+ random_seed
+ |
+
+
+
+ The random seed to use for reproducible data generation. +
+
+ TYPE:
+ |
+
| RETURNS | +DESCRIPTION | +
|---|---|
+ BaseModel
+ |
+
+
+
+ The generated CDS FHIR data. +
+
+ TYPE:
+ |
+
healthchain/data_generators/cdsdatagenerator.pyset_workflow(workflow)
+
+Sets the current workflow to be used for data generation.
+ + +| PARAMETER | +DESCRIPTION | +
|---|---|
+ workflow
+ |
+
+
+
+ The name of the workflow to set. +
+
+ TYPE:
+ |
+
healthchain/data_generators/cdsdatagenerator.pyClassGenerator
+
+
+
+ Bases: BaseGenerator
A generator class for creating FHIR Class resources.
+ + +| METHOD | +DESCRIPTION | +
|---|---|
generate |
+
+
+
+ Generates a FHIR Class resource. + |
+
healthchain/data_generators/encountergenerators.pyEncounterGenerator
+
+
+
+ Bases: BaseGenerator
A generator class for creating FHIR Encounter resources.
+ + +| METHOD | +DESCRIPTION | +
|---|---|
generate |
+
+
+
+ Optional[list] = None, random_seed: Optional[int] = None) -> Encounter: +Generates a FHIR Encounter resource with optional constraints and random_seed. + |
+
healthchain/data_generators/encountergenerators.pyEncounterLocationGenerator
+
+
+
+ Bases: BaseGenerator
A generator class for creating FHIR EncounterLocation resources.
+ + +| METHOD | +DESCRIPTION | +
|---|---|
generate |
+
+
+
+ Generates a FHIR EncounterLocation resource. + |
+
healthchain/data_generators/encountergenerators.pyEncounterPriorityGenerator
+
+
+
+ Bases: BaseGenerator
A generator class for creating FHIR EncounterPriority resources.
+ + +| METHOD | +DESCRIPTION | +
|---|---|
generate |
+
+
+
+ Generates a FHIR EncounterPriority resource. + |
+
healthchain/data_generators/encountergenerators.pyEncounterTypeGenerator
+
+
+
+ Bases: BaseGenerator
A generator class for creating FHIR EncounterType resources.
+ + +| METHOD | +DESCRIPTION | +
|---|---|
generate |
+
+
+
+ Generates a FHIR EncounterType resource. + |
+
healthchain/data_generators/encountergenerators.pyPeriodGenerator
+
+
+
+ Bases: BaseGenerator
A generator class for creating FHIR Period resources.
+ + +| METHOD | +DESCRIPTION | +
|---|---|
generate |
+
+
+
+ Generates a FHIR Period resource with random start and end times. + |
+
healthchain/data_generators/encountergenerators.pyCcdData
+
+
+
+ Bases: BaseModel
Data model for CCD (Continuity of Care Document) that can be converted to CDA.
+ + +| ATTRIBUTE | +DESCRIPTION | +
|---|---|
concepts |
+
+
+
+ Container for medical concepts (problems, allergies, medications) +extracted from the document. Defaults to empty ConceptLists. +
+
+ TYPE:
+ |
+
note |
+
+
+
+ The clinical note text, either as a string or +dictionary of sections. Defaults to None. +
+
+ TYPE:
+ |
+
cda_xml |
+
+
+
+ The raw CDA XML string representation of the document. +Defaults to None. +
+
+ TYPE:
+ |
+
healthchain/models/data/ccddata.pyCdsFhirData
+
+
+
+ Bases: BaseModel
Data model for CDS FHIR data, matching the expected fields in CDSRequests.
+ + +| ATTRIBUTE | +DESCRIPTION | +
|---|---|
context |
+
+
+
+ A dictionary containing contextual information for the CDS request. +
+
+ TYPE:
+ |
+
prefetch |
+
+
+
+ A Bundle object containing prefetched FHIR resources. +
+
+ TYPE:
+ |
+
| METHOD | +DESCRIPTION | +
|---|---|
create |
+
+
+
+ Dict, prefetch: Dict): Class method to create a CdsFhirData instance. + |
+
model_dump |
+
+
+
+ Returns a dictionary representation of the model. + |
+
model_dump_json |
+
+
+
+ Returns a JSON string representation of the model. + |
+
model_dump_prefetch |
+
+
+
+ Returns a dictionary representation of the prefetch Bundle. + |
+
healthchain/models/data/cdsfhirdata.pyAllergyConcept
+
+
+
+ Bases: Concept
Contains allergy specific fields
+Defaults allergy type to propensity to adverse reactions in SNOMED CT
+ + + + + + +healthchain/models/data/concept.pyConcept
+
+
+
+ Bases: BaseModel
A more lenient, system agnostic representation of a concept e.g. problems, medications, allergies +that can be converted to CDA or FHIR
+ + + + + + +healthchain/models/data/concept.pyConceptLists
+
+
+
+ Bases: BaseModel
Container for lists of medical concepts extracted from text.
+ + + + + + +healthchain/models/data/concept.pyDataType
+
+
+MedicationConcept
+
+
+
+ Bases: Concept
Contains medication specific fields
+ + + + + + +healthchain/models/data/concept.pyProblemConcept
+
+
+
+ Bases: Concept
Contains problem/condition specific fields
+ + + + + + +healthchain/models/data/concept.pyBasePipeline
+
+
+
+ Bases: Generic[T], ABC
Abstract base class for creating and managing data processing pipelines.
+The BasePipeline class provides a framework for building modular data processing pipelines +by allowing users to add, remove, and configure components with defined dependencies and +execution order. Components can be added at specific positions, grouped into stages, and +connected via input/output connectors.
+This is an abstract base class that should be subclassed to create specific pipeline +implementations.
+ + +| ATTRIBUTE | +DESCRIPTION | +
|---|---|
_components |
+
+
+
+ Ordered list of pipeline components +
+
+ TYPE:
+ |
+
_stages |
+
+
+
+ Components grouped by processing stage +
+
+ TYPE:
+ |
+
_built_pipeline |
+
+
+
+ Compiled pipeline function +
+
+ TYPE:
+ |
+
_input_connector |
+
+
+
+ Connector for processing input data +
+
+ TYPE:
+ |
+
_output_connector |
+
+
+
+ Connector for processing output data +
+
+ TYPE:
+ |
+
_output_template |
+
+
+
+ Template string for formatting pipeline outputs +
+
+ TYPE:
+ |
+
_model_config |
+
+
+
+ Configuration for the pipeline model +
+
+ TYPE:
+ |
+
++++++class MyPipeline(BasePipeline[str]): +... def configure_pipeline(self, config: ModelConfig) -> None: +... self.add_node(preprocess, stage="preprocessing") +... self.add_node(process, stage="processing") +... self.add_node(postprocess, stage="postprocessing") +... +pipeline = MyPipeline() +result = pipeline("input text")
+
healthchain/pipeline/base.py77 + 78 + 79 + 80 + 81 + 82 + 83 + 84 + 85 + 86 + 87 + 88 + 89 + 90 + 91 + 92 + 93 + 94 + 95 + 96 + 97 + 98 + 99 +100 +101 +102 +103 +104 +105 +106 +107 +108 +109 +110 +111 +112 +113 +114 +115 +116 +117 +118 +119 +120 +121 +122 +123 +124 +125 +126 +127 +128 +129 +130 +131 +132 +133 +134 +135 +136 +137 +138 +139 +140 +141 +142 +143 +144 +145 +146 +147 +148 +149 +150 +151 +152 +153 +154 +155 +156 +157 +158 +159 +160 +161 +162 +163 +164 +165 +166 +167 +168 +169 +170 +171 +172 +173 +174 +175 +176 +177 +178 +179 +180 +181 +182 +183 +184 +185 +186 +187 +188 +189 +190 +191 +192 +193 +194 +195 +196 +197 +198 +199 +200 +201 +202 +203 +204 +205 +206 +207 +208 +209 +210 +211 +212 +213 +214 +215 +216 +217 +218 +219 +220 +221 +222 +223 +224 +225 +226 +227 +228 +229 +230 +231 +232 +233 +234 +235 +236 +237 +238 +239 +240 +241 +242 +243 +244 +245 +246 +247 +248 +249 +250 +251 +252 +253 +254 +255 +256 +257 +258 +259 +260 +261 +262 +263 +264 +265 +266 +267 +268 +269 +270 +271 +272 +273 +274 +275 +276 +277 +278 +279 +280 +281 +282 +283 +284 +285 +286 +287 +288 +289 +290 +291 +292 +293 +294 +295 +296 +297 +298 +299 +300 +301 +302 +303 +304 +305 +306 +307 +308 +309 +310 +311 +312 +313 +314 +315 +316 +317 +318 +319 +320 +321 +322 +323 +324 +325 +326 +327 +328 +329 +330 +331 +332 +333 +334 +335 +336 +337 +338 +339 +340 +341 +342 +343 +344 +345 +346 +347 +348 +349 +350 +351 +352 +353 +354 +355 +356 +357 +358 +359 +360 +361 +362 +363 +364 +365 +366 +367 +368 +369 +370 +371 +372 +373 +374 +375 +376 +377 +378 +379 +380 +381 +382 +383 +384 +385 +386 +387 +388 +389 +390 +391 +392 +393 +394 +395 +396 +397 +398 +399 +400 +401 +402 +403 +404 +405 +406 +407 +408 +409 +410 +411 +412 +413 +414 +415 +416 +417 +418 +419 +420 +421 +422 +423 +424 +425 +426 +427 +428 +429 +430 +431 +432 +433 +434 +435 +436 +437 +438 +439 +440 +441 +442 +443 +444 +445 +446 +447 +448 +449 +450 +451 +452 +453 +454 +455 +456 +457 +458 +459 +460 +461 +462 +463 +464 +465 +466 +467 +468 +469 +470 +471 +472 +473 +474 +475 +476 +477 +478 +479 +480 +481 +482 +483 +484 +485 +486 +487 +488 +489 +490 +491 +492 +493 +494 +495 +496 +497 +498 +499 +500 +501 +502 +503 +504 +505 +506 +507 +508 +509 +510 +511 +512 +513 +514 +515 +516 +517 +518 +519 +520 +521 +522 +523 +524 +525 +526 +527 +528 +529 +530 +531 +532 +533 +534 +535 +536 +537 +538 +539 +540 +541 +542 +543 +544 +545 +546 +547 +548 +549 +550 +551 +552 +553 +554 +555 +556 +557 +558 +559 +560 +561 +562 +563 +564 +565 +566 +567 +568 +569 +570 +571 +572 +573 +574 +575 +576 +577 +578 +579 +580 +581 +582 +583 +584 +585 +586 +587 +588 +589 +590 +591 +592 +593 +594 +595 +596 +597 +598 +599 +600 +601 +602 +603 +604 +605 +606 +607 +608 +609 +610 +611 +612 +613 +614 +615 +616 +617 +618 +619 +620 +621 +622 +623 +624 +625 +626 +627 +628 +629 +630 +631 +632 +633 +634 +635 +636 +637 +638 +639 +640 +641 +642 +643 +644 +645 +646 +647 +648 +649 +650 +651 +652 +653 +654 +655 +656 +657 +658 +659 +660 +661 +662 +663 +664 +665 +666 +667 +668 +669 +670 +671 +672 +673 +674 +675 +676 +677 +678 +679 +680 +681 +682 +683 +684 +685 +686 +687 +688 +689 +690 +691 +692 +693 +694 +695 +696 +697 +698 +699 +700 +701 +702 +703 +704 +705 +706 +707 +708 +709 +710 +711 +712 +713 +714 +715 +716 +717 +718 +719 +720 +721 +722 +723 +724 +725 +726 +727 +728 +729 +730 +731 +732 +733 +734 +735 +736 +737 +738 +739 +740 +741 +742 +743 +744 +745 +746 +747 +748 +749 +750 +751 +752 +753 +754 +755 +756 +757 +758 +759 +760 +761 +762 +763 +764 +765 +766 +767 +768 +769 +770 +771 +772 +773 +774 +775 +776 +777 +778 +779 +780 +781 +782 +783 +784 +785 +786 +787 +788 +789 | |
stages
+
+
+ property
+ writable
+
+
+Returns a human-readable representation of the pipeline stages.
+add_input(connector)
+
+Adds an input connector to the pipeline.
+This method sets the input connector for the pipeline, which is responsible +for processing the input data before it's passed to the pipeline components.
+ + +| PARAMETER | +DESCRIPTION | +
|---|---|
+ connector
+ |
+
+
+
+ The input connector to be added to the pipeline. +
+
+ TYPE:
+ |
+
| RETURNS | +DESCRIPTION | +
|---|---|
+
+ None
+
+ |
+
+
+
+ None + |
+
Only one input connector can be set for the pipeline. If this method is +called multiple times, the last connector will overwrite the previous ones.
+healthchain/pipeline/base.pyadd_node(component=None, *, position='default', reference=None, stage=None, name=None, input_model=None, output_model=None, dependencies=[])
+
+Adds a component node to the pipeline.
+ + +| PARAMETER | +DESCRIPTION | +
|---|---|
+ component
+ |
+
+
+
+ The component to be added. It can be either a BaseComponent object or a callable function. +Defaults to None. +
+
+ TYPE:
+ |
+
+ position
+ |
+
+
+
+ The position at which the component should be added in the pipeline. +Valid values are "default", "first", "last", "after", and "before". +Defaults to "default". +
+
+ TYPE:
+ |
+
+ reference
+ |
+
+
+
+ The name of the component after or before which the new component should be added. +Only applicable when position is "after" or "before". +Defaults to None. +
+
+ TYPE:
+ |
+
+ stage
+ |
+
+
+
+ The stage to which the component belongs. +Defaults to None. +
+
+ TYPE:
+ |
+
+ name
+ |
+
+
+
+ The name of the component. +Defaults to None, in which case the name of the function will be used. +
+
+ TYPE:
+ |
+
+ input_model
+ |
+
+
+
+ The input Pydantic model class for validating the input data. +Defaults to None. +
+
+ TYPE:
+ |
+
+ output_model
+ |
+
+
+
+ The output Pydantic model class for validating the output data. +Defaults to None. +
+
+ TYPE:
+ |
+
+ dependencies
+ |
+
+
+
+ The list of component names that this component depends on. +Defaults to an empty list. +
+
+ TYPE:
+ |
+
| RETURNS | +DESCRIPTION | +
|---|---|
+
+ None
+
+ |
+
+
+
+ The original component if component is None, otherwise the wrapper function. + |
+
healthchain/pipeline/base.py460 +461 +462 +463 +464 +465 +466 +467 +468 +469 +470 +471 +472 +473 +474 +475 +476 +477 +478 +479 +480 +481 +482 +483 +484 +485 +486 +487 +488 +489 +490 +491 +492 +493 +494 +495 +496 +497 +498 +499 +500 +501 +502 +503 +504 +505 +506 +507 +508 +509 +510 +511 +512 +513 +514 +515 +516 +517 +518 +519 +520 +521 +522 +523 +524 +525 +526 +527 +528 +529 +530 +531 +532 +533 +534 +535 +536 +537 +538 +539 +540 +541 +542 +543 +544 +545 +546 +547 +548 +549 +550 +551 +552 +553 +554 +555 +556 +557 +558 +559 +560 +561 +562 +563 +564 +565 +566 | |
add_output(connector)
+
+Adds an output connector to the pipeline.
+This method sets the output connector for the pipeline, which is responsible +for processing the output data after it has passed through all pipeline components.
+ + +| PARAMETER | +DESCRIPTION | +
|---|---|
+ connector
+ |
+
+
+
+ The output connector to be added to the pipeline. +
+
+ TYPE:
+ |
+
| RETURNS | +DESCRIPTION | +
|---|---|
+
+ None
+
+ |
+
+
+
+ None + |
+
Only one output connector can be set for the pipeline. If this method is +called multiple times, the last connector will overwrite the previous ones.
+healthchain/pipeline/base.pybuild()
+
+Builds and returns a pipeline function that applies a series of components to the input data. +Returns: + pipeline: A function that takes input data and applies the ordered components to it. +Raises: + ValueError: If a circular dependency is detected among the components.
+ +healthchain/pipeline/base.pyconfigure_pipeline(model_config)
+
+
+ abstractmethod
+
+
+Configure the pipeline based on the provided model configuration.
+This method should be implemented by subclasses to add specific components +and configure the pipeline according to the given model configuration. +The configuration typically involves: +1. Setting up input/output connectors +2. Adding model components based on the model source +3. Adding any additional processing nodes +4. Configuring the pipeline stages and execution order
+ + +| PARAMETER | +DESCRIPTION | +
|---|---|
+ model_config
+ |
+
+
+
+ Configuration object containing: +- source: Model source (e.g. huggingface, spacy, langchain) +- model: Model identifier or path +- task: Optional task name (e.g. summarization, ner) +- path: Optional local path to model files +- kwargs: Additional model configuration parameters +
+
+ TYPE:
+ |
+
| RETURNS | +DESCRIPTION | +
|---|---|
+
+ None
+
+ |
+
+
+
+ None + |
+
| RAISES | +DESCRIPTION | +
|---|---|
+
+ NotImplementedError
+
+ |
+
+
+
+ If the method is not implemented by a subclass. + |
+
++++++def configure_pipeline(self, config: ModelConfig): +... # Add FHIR connector for input/output +... connector = FhirConnector() +... self.add_input(connector) +... +... # Add model component +... model = self.get_model_component(config) +... self.add_node(model, stage="processing") +... +... # Add output formatting +... self.add_node(OutputFormatter(), stage="formatting") +... self.add_output(connector)
+
healthchain/pipeline/base.pyfrom_local_model(path, source, task=None, template=None, template_path=None, **kwargs)
+
+
+ classmethod
+
+
+Load pipeline from a local model path.
+ + +| PARAMETER | +DESCRIPTION | +
|---|---|
+ path
+ |
+
+
+
+ Path to local model files/directory +
+
+ TYPE:
+ |
+
+ source
+ |
+
+
+
+ Model source (e.g. "huggingface", "spacy") +
+
+ TYPE:
+ |
+
+ task
+ |
+
+
+
+ Task identifier for the model. Defaults to None. +
+
+ TYPE:
+ |
+
+ template
+ |
+
+
+
+ Optional template string for formatting model output. +
+
+ TYPE:
+ |
+
+ template_path
+ |
+
+
+
+ Optional path to template file for formatting model output. +
+
+ TYPE:
+ |
+
+ **kwargs
+ |
+
+
+
+ Additional configuration options passed to the model. e.g. temperature, max_length, etc. +
+
+ TYPE:
+ |
+
| RETURNS | +DESCRIPTION | +
|---|---|
+ BasePipeline
+ |
+
+
+
+ Configured pipeline instance. +
+
+ TYPE:
+ |
+
| RAISES | +DESCRIPTION | +
|---|---|
+
+ ValueError
+
+ |
+
+
+
+ If source is not a valid ModelSource. + |
+
Examples:
+>>> # Load local HuggingFace model
+>>> pipeline = Pipeline.from_local_model(
+... "models/my_summarizer",
+... source="huggingface",
+... task="summarization",
+... temperature=0.7
+... )
+>>>
+>>> # Load local SpaCy model
+>>> pipeline = Pipeline.from_local_model(
+... "models/en_core_sci_md",
+... source="spacy",
+... disable=["parser"]
+... )
+>>>
+>>> # Load with output template
+>>> template = '''{"summary": "{{ model_output }}"}'''
+>>> pipeline = Pipeline.from_local_model(
+... "models/gpt_model",
+... source="huggingface",
+... template=template
+... )
+healthchain/pipeline/base.py279 +280 +281 +282 +283 +284 +285 +286 +287 +288 +289 +290 +291 +292 +293 +294 +295 +296 +297 +298 +299 +300 +301 +302 +303 +304 +305 +306 +307 +308 +309 +310 +311 +312 +313 +314 +315 +316 +317 +318 +319 +320 +321 +322 +323 +324 +325 +326 +327 +328 +329 +330 +331 +332 +333 +334 +335 +336 +337 +338 +339 +340 +341 +342 +343 | |
from_model_id(model_id, source='huggingface', task='text-generation', template=None, template_path=None, **kwargs)
+
+
+ classmethod
+
+
+Load pipeline from a model identifier.
+ + +| PARAMETER | +DESCRIPTION | +
|---|---|
+ model_id
+ |
+
+
+
+ Model identifier (e.g. HuggingFace model ID, SpaCy model name) +
+
+ TYPE:
+ |
+
+ source
+ |
+
+
+
+ Model source. Defaults to "huggingface". +Can be "huggingface", "spacy". +
+
+ TYPE:
+ |
+
+ task
+ |
+
+
+
+ Task identifier for the model. Defaults to "text-generation". +
+
+ TYPE:
+ |
+
+ template
+ |
+
+
+
+ Optional template string for formatting model output. +
+
+ TYPE:
+ |
+
+ template_path
+ |
+
+
+
+ Optional path to template file for formatting model output. +
+
+ TYPE:
+ |
+
+ **kwargs
+ |
+
+
+
+ Additional configuration options passed to the model. e.g. temperature, max_length, etc. +
+
+ TYPE:
+ |
+
| RETURNS | +DESCRIPTION | +
|---|---|
+ BasePipeline
+ |
+
+
+
+ Configured pipeline instance. +
+
+ TYPE:
+ |
+
| RAISES | +DESCRIPTION | +
|---|---|
+
+ ValueError
+
+ |
+
+
+
+ If source is not a valid ModelSource. + |
+
Examples:
+>>> # Load HuggingFace model
+>>> pipeline = Pipeline.from_model_id(
+... "facebook/bart-large-cnn",
+... task="summarization",
+... temperature=0.7
+... )
+>>>
+>>> # Load SpaCy model
+>>> pipeline = Pipeline.from_model_id(
+... "en_core_sci_md",
+... source="spacy",
+... disable=["parser"]
+... )
+>>>
+>>> # Load with output template
+>>> template = '''{"summary": "{{ model_output }}"}'''
+>>> pipeline = Pipeline.from_model_id(
+... "gpt-3.5-turbo",
+... source="huggingface",
+... template=template
+... )
+healthchain/pipeline/base.py214 +215 +216 +217 +218 +219 +220 +221 +222 +223 +224 +225 +226 +227 +228 +229 +230 +231 +232 +233 +234 +235 +236 +237 +238 +239 +240 +241 +242 +243 +244 +245 +246 +247 +248 +249 +250 +251 +252 +253 +254 +255 +256 +257 +258 +259 +260 +261 +262 +263 +264 +265 +266 +267 +268 +269 +270 +271 +272 +273 +274 +275 +276 +277 | |
load(pipeline, source, task='text-generation', template=None, template_path=None, **kwargs)
+
+
+ classmethod
+
+
+Load a pipeline from a pre-built pipeline object (e.g. LangChain chain or HuggingFace pipeline).
+ + +| PARAMETER | +DESCRIPTION | +
|---|---|
+ pipeline
+ |
+
+
+
+ A callable pipeline object (e.g. LangChain chain, HuggingFace pipeline) +
+
+ TYPE:
+ |
+
+ source
+ |
+
+
+
+ Source of the pipeline. Can be "langchain" or "huggingface". +
+
+ TYPE:
+ |
+
+ task
+ |
+
+
+
+ Task identifier used to retrieve model outputs. +Defaults to "text-generation". +
+
+ TYPE:
+ |
+
+ template
+ |
+
+
+
+ Template string for formatting outputs. +Defaults to None. +
+
+ TYPE:
+ |
+
+ template_path
+ |
+
+
+
+ Path to template file. +Defaults to None. +
+
+ TYPE:
+ |
+
+ **kwargs
+ |
+
+
+
+ Additional configuration options passed to the pipeline. +
+
+ TYPE:
+ |
+
| RETURNS | +DESCRIPTION | +
|---|---|
+ BasePipeline
+ |
+
+
+
+ Configured pipeline instance. +
+
+ TYPE:
+ |
+
| RAISES | +DESCRIPTION | +
|---|---|
+
+ ValueError
+
+ |
+
+
+
+ If pipeline is not callable or source is invalid. + |
+
Examples:
+>>> # Load LangChain pipeline
+>>> from langchain_core.prompts import ChatPromptTemplate
+>>> from langchain_openai import ChatOpenAI
+>>> chain = ChatPromptTemplate.from_template("What is {input}?") | ChatOpenAI()
+>>> pipeline = Pipeline.load(chain, source="langchain", temperature=0.7)
+>>>
+>>> # Load HuggingFace pipeline
+>>> from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
+>>> tokenizer = AutoTokenizer.from_pretrained("gpt2")
+>>> model = AutoModelForCausalLM.from_pretrained("gpt2")
+>>> pipe = pipeline("text-generation", model=model, tokenizer=tokenizer, max_new_tokens=10)
+>>> pipeline = Pipeline.load(pipe, source="huggingface")
+healthchain/pipeline/base.py141 +142 +143 +144 +145 +146 +147 +148 +149 +150 +151 +152 +153 +154 +155 +156 +157 +158 +159 +160 +161 +162 +163 +164 +165 +166 +167 +168 +169 +170 +171 +172 +173 +174 +175 +176 +177 +178 +179 +180 +181 +182 +183 +184 +185 +186 +187 +188 +189 +190 +191 +192 +193 +194 +195 +196 +197 +198 +199 +200 +201 +202 +203 +204 +205 +206 +207 +208 +209 +210 +211 +212 | |
remove(component_name)
+
+Removes a component from the pipeline.
+ + +| PARAMETER | +DESCRIPTION | +
|---|---|
+ component_name
+ |
+
+
+
+ The name of the component to be removed. +
+
+ TYPE:
+ |
+
| RAISES | +DESCRIPTION | +
|---|---|
+
+ ValueError
+
+ |
+
+
+
+ If the component is not found in the pipeline. + |
+
| RETURNS | +DESCRIPTION | +
|---|---|
+
+ None
+
+ |
+
+
+
+ None + |
+
DEBUG: When the component is successfully removed. +WARNING: If the component fails to be removed after attempting to do so.
+healthchain/pipeline/base.pyreplace(old_component_name, new_component)
+
+Replaces a component in the pipeline with a new component.
+ + +| PARAMETER | +DESCRIPTION | +
|---|---|
+ old_component_name
+ |
+
+
+
+ The name of the component to be replaced. +
+
+ TYPE:
+ |
+
+ new_component
+ |
+
+
+
+ The new component to replace the old component with. +
+
+ TYPE:
+ |
+
| RETURNS | +DESCRIPTION | +
|---|---|
+
+ None
+
+ |
+
+
+
+ None + |
+
| RAISES | +DESCRIPTION | +
|---|---|
+
+ ValueError
+
+ |
+
+
+
+ If the old component is not found in the pipeline. + |
+
+
+ ValueError
+
+ |
+
+
+
+ If the new component is not a BaseComponent or a callable. + |
+
+
+ ValueError
+
+ |
+
+
+
+ If the new component callable doesn't have the correct signature. + |
+
DEBUG: When the component is successfully replaced.
+healthchain/pipeline/base.py666 +667 +668 +669 +670 +671 +672 +673 +674 +675 +676 +677 +678 +679 +680 +681 +682 +683 +684 +685 +686 +687 +688 +689 +690 +691 +692 +693 +694 +695 +696 +697 +698 +699 +700 +701 +702 +703 +704 +705 +706 +707 +708 +709 +710 +711 +712 +713 +714 +715 +716 +717 +718 +719 +720 +721 +722 +723 +724 +725 +726 +727 +728 +729 +730 +731 +732 +733 +734 +735 +736 +737 | |
ModelConfig
+
+
+
+ dataclass
+
+
+Configuration for model initialization
+ + + + + + +healthchain/pipeline/base.pyModelSource
+
+
+
+ Bases: Enum
Enumeration of supported model sources
+ + + + + + +healthchain/pipeline/base.pyPipeline
+
+
+
+ Bases: BasePipeline, Generic[T]
Default Pipeline class for creating a basic data processing pipeline. +This class inherits from BasePipeline and provides a default implementation +of the configure_pipeline method, which does not add any specific components.
+ + + + + + +healthchain/pipeline/base.pyconfigure_pipeline(model_path)
+
+Configures the pipeline by adding components based on the provided model path. +This default implementation does not add any specific components.
+ + +| PARAMETER | +DESCRIPTION | +
|---|---|
+ model_path
+ |
+
+
+
+ The path to the model used for configuring the pipeline. +
+
+ TYPE:
+ |
+
healthchain/pipeline/base.pyPipelineNode
+
+
+
+ dataclass
+
+
+
+ Bases: Generic[T]
Represents a node in a pipeline.
+ + +| ATTRIBUTE | +DESCRIPTION | +
|---|---|
func |
+
+
+
+ The function to be applied to the data. +
+
+ TYPE:
+ |
+
position |
+
+
+
+ The position of the node in the pipeline. Defaults to "default". +
+
+ TYPE:
+ |
+
reference |
+
+
+
+ The reference for the relative position of the node. Name should be the "name" attribute of another node. Defaults to None. +
+
+ TYPE:
+ |
+
stage |
+
+
+
+ The stage of the node in the pipeline. Group nodes by stage e.g. "preprocessing". Defaults to None. +
+
+ TYPE:
+ |
+
name |
+
+
+
+ The name of the node. Defaults to None. +
+
+ TYPE:
+ |
+
dependencies |
+
+
+
+ The list of dependencies for the node. Defaults to an empty list. +
+
+ TYPE:
+ |
+
healthchain/pipeline/base.pyService
+
+
+A service wrapper which registers routes and starts a FastAPI service
+Parameters: +endpoints (Dict[str, Enpoint]): the list of endpoints to register, must be a dictionary +of Endpoint objects. Should have human-readable keys e.g. ["info", "service_mount"]
+ + + + + + +healthchain/service/service.py21 + 22 + 23 + 24 + 25 + 26 + 27 + 28 + 29 + 30 + 31 + 32 + 33 + 34 + 35 + 36 + 37 + 38 + 39 + 40 + 41 + 42 + 43 + 44 + 45 + 46 + 47 + 48 + 49 + 50 + 51 + 52 + 53 + 54 + 55 + 56 + 57 + 58 + 59 + 60 + 61 + 62 + 63 + 64 + 65 + 66 + 67 + 68 + 69 + 70 + 71 + 72 + 73 + 74 + 75 + 76 + 77 + 78 + 79 + 80 + 81 + 82 + 83 + 84 + 85 + 86 + 87 + 88 + 89 + 90 + 91 + 92 + 93 + 94 + 95 + 96 + 97 + 98 + 99 +100 +101 +102 +103 +104 +105 +106 | |
run(config=None)
+
+Starts server on uvicorn.
+Parameters: +config (Dict): kwargs to pass into uvicorn.
+ +healthchain/service/service.pyClinicalDecisionSupport
+
+
+
+ Bases: BaseUseCase
Implements EHR backend simulator for Clinical Decision Support (CDS)
+ + +| PARAMETER | +DESCRIPTION | +
|---|---|
+ service_api
+ |
+
+
+
+ the function body to inject into the main service +
+
+ TYPE:
+ |
+
+ service_config
+ |
+
+
+
+ the config kwargs for the uvicorn server passed into service +
+
+ TYPE:
+ |
+
+ service
+ |
+
+
+
+ the service runner object +
+
+ TYPE:
+ |
+
+ client
+ |
+
+
+
+ the client runner object +
+
+ TYPE:
+ |
+
See https://cds-hooks.org/ for specification
+ + + + + + +healthchain/use_cases/cds.py85 + 86 + 87 + 88 + 89 + 90 + 91 + 92 + 93 + 94 + 95 + 96 + 97 + 98 + 99 +100 +101 +102 +103 +104 +105 +106 +107 +108 +109 +110 +111 +112 +113 +114 +115 +116 +117 +118 +119 +120 +121 +122 +123 +124 +125 +126 +127 +128 +129 +130 +131 +132 +133 +134 +135 +136 +137 +138 +139 +140 +141 +142 +143 +144 +145 +146 +147 +148 +149 +150 +151 +152 +153 +154 +155 +156 +157 +158 +159 +160 +161 +162 +163 +164 +165 +166 +167 +168 +169 +170 +171 +172 +173 +174 +175 +176 +177 +178 +179 +180 +181 +182 +183 +184 +185 +186 +187 +188 +189 +190 +191 +192 +193 +194 +195 +196 +197 +198 +199 +200 +201 +202 +203 +204 +205 +206 +207 +208 +209 +210 +211 +212 +213 +214 +215 +216 +217 +218 | |
cds_discovery()
+
+CDS discovery endpoint for FastAPI app, should be mounted to /cds-services
+ +healthchain/use_cases/cds.pycds_service(id, request)
+
+CDS service endpoint for FastAPI app, mounted to /cds-services/{id}
+This method handles the execution of a specific CDS service. It validates the +service configuration, checks the input parameters, executes the service +function, and ensures the correct response type is returned.
+ + +| PARAMETER | +DESCRIPTION | +
|---|---|
+ id
+ |
+
+
+
+ The unique identifier of the CDS service to be executed. +
+
+ TYPE:
+ |
+
+ request
+ |
+
+
+
+ The request object containing the input data for the CDS service. +
+
+ TYPE:
+ |
+
| RETURNS | +DESCRIPTION | +
|---|---|
+ CDSResponse
+ |
+
+
+
+ The response object containing the cards generated by the CDS service. +
+
+ TYPE:
+ |
+
| RAISES | +DESCRIPTION | +
|---|---|
+
+ AssertionError
+
+ |
+
+
+
+ If the service function is not properly configured. + |
+
+
+ TypeError
+
+ |
+
+
+
+ If the input or output types do not match the expected types. + |
+
This method performs several checks to ensure the integrity of the service: +1. Verifies that the service API is configured. +2. Validates the signature of the service function. +3. Ensures the service function accepts a CDSRequest as its first argument. +4. Verifies that the service function returns a CDSResponse.
+healthchain/use_cases/cds.pyClinicalDecisionSupportStrategy
+
+
+
+ Bases: BaseStrategy
Handles the request construction and validation
+ + + + + + +healthchain/use_cases/cds.pyconstruct_request(data, workflow)
+
+Constructs a HL7-compliant CDS request based on workflow.
+ + +| PARAMETER | +DESCRIPTION | +
|---|---|
+ data
+ |
+
+
+
+ FHIR data to be injected in request. +
+
+ TYPE:
+ |
+
+ workflow
+ |
+
+
+
+ The CDS hook name, e.g. patient-view. +
+
+ TYPE:
+ |
+
| RETURNS | +DESCRIPTION | +
|---|---|
+ CDSRequest
+ |
+
+
+
+ A Pydantic model that wraps a CDS request for REST +
+
+ TYPE:
+ |
+
| RAISES | +DESCRIPTION | +
|---|---|
+
+ ValueError
+
+ |
+
+
+
+ If the workflow is invalid or the data does not validate properly. + |
+
healthchain/use_cases/cds.pyCDSRequest
+
+
+
+ Bases: BaseModel
A model representing the data structure for a CDS service call, triggered by specific hooks +within a healthcare application.
+ + +| ATTRIBUTE | +DESCRIPTION | +
|---|---|
hook |
+
+
+
+ The hook that triggered this CDS Service call. For example, 'patient-view'. +
+
+ TYPE:
+ |
+
hookInstance |
+
+
+
+ A universally unique identifier for this particular hook call. +
+
+ TYPE:
+ |
+
fhirServer |
+
+
+
+ The base URL of the CDS Client's FHIR server. This field is required if
+
+ TYPE:
+ |
+
fhirAuthorization |
+
+
+
+ Optional authorization details providing a bearer access token for FHIR resources. +
+
+ TYPE:
+ |
+
context |
+
+
+
+ Hook-specific contextual data required by the CDS service. +
+
+ TYPE:
+ |
+
prefetch |
+
+
+
+ Optional FHIR data that was prefetched by the CDS Client. +
+
+ TYPE:
+ |
+
Documentation: https://cds-hooks.org/specification/current/#http-request_1
+ + + + + + +healthchain/models/requests/cdsrequest.pyAction
+
+
+
+ Bases: BaseModel
Within a suggestion, all actions are logically AND'd together, such that a user selecting a +suggestion selects all of the actions within it. When a suggestion contains multiple actions, +the actions SHOULD be processed as per FHIR's rules for processing transactions with the CDS +Client's fhirServer as the base url for the inferred full URL of the transaction bundle entries.
+https://cds-hooks.org/specification/current/#action
+ + + + + + +healthchain/models/responses/cdsresponse.pyActionTypeEnum
+
+
+
+ Bases: str, Enum
The type of action being performed
+ + + + + + +healthchain/models/responses/cdsresponse.pyCDSResponse
+
+
+
+ Bases: BaseModel
Represents the response from a CDS service.
+This class models the structure of a CDS Hooks response, which includes +cards for displaying information or suggestions to the user, and optional +system actions that can be executed automatically.
+ + +| ATTRIBUTE | +DESCRIPTION | +
|---|---|
cards |
+
+
+
+ A list of Card objects to be displayed to the end user. +Default is an empty list. +
+
+ TYPE:
+ |
+
systemActions |
+
+
+
+ A list of Action objects representing +actions that the CDS Client should execute as part of performing +the decision support requested. This field is optional. +
+
+ TYPE:
+ |
+
For more information, see: +https://cds-hooks.org/specification/current/#cds-service-response
+ + + + + + +healthchain/models/responses/cdsresponse.pyCard
+
+
+
+ Bases: BaseModel
Cards can provide a combination of information (for reading), suggested actions +(to be applied if a user selects them), and links (to launch an app if the user selects them). +The CDS Client decides how to display cards, but this specification recommends displaying suggestions +using buttons, and links using underlined text.
+https://cds-hooks.org/specification/current/#card-attributes
+ + + + + + +healthchain/models/responses/cdsresponse.pyIndicatorEnum
+
+
+
+ Bases: str, Enum
Urgency/importance of what Card conveys. +Allowed values, in order of increasing urgency, are: info, warning, critical. +The CDS Client MAY use this field to help make UI display decisions such as sort order or coloring.
+ + + + + + +healthchain/models/responses/cdsresponse.pyLink
+
+
+
+ Bases: BaseModel
CDS Client support for appContext requires additional coordination with the authorization +server that is not described or specified in CDS Hooks nor SMART.
+Autolaunchable is experimental
+https://cds-hooks.org/specification/current/#link
+ + + + + + +healthchain/models/responses/cdsresponse.pyLinkTypeEnum
+
+
+
+ Bases: str, Enum
The type of the given URL. There are two possible values for this field. +A type of absolute indicates that the URL is absolute and should be treated as-is. +A type of smart indicates that the URL is a SMART app launch URL and the CDS Client +should ensure the SMART app launch URL is populated with the appropriate SMART +launch parameters.
+ + + + + + +healthchain/models/responses/cdsresponse.pySelectionBehaviorEnum
+
+
+
+ Bases: str, Enum
Describes the intended selection behavior of the suggestions in the card. +Allowed values are: at-most-one, indicating that the user may choose none or +at most one of the suggestions; any, indicating that the end user may choose +any number of suggestions including none of them and all of them. +CDS Clients that do not understand the value MUST treat the card as an error.
+ + + + + + +healthchain/models/responses/cdsresponse.pySimpleCoding
+
+
+
+ Bases: BaseModel
The Coding data type captures the concept of a code. This coding type is a standalone data type +in CDS Hooks modeled after a trimmed down version of the FHIR Coding data type.
+ + + + + + +healthchain/models/responses/cdsresponse.pySource
+
+
+
+ Bases: BaseModel
Grouping structure for the Source of the information displayed on this card. +The source should be the primary source of guidance for the decision support Card represents.
+https://cds-hooks.org/specification/current/#source
+ + + + + + +healthchain/models/responses/cdsresponse.pySuggestion
+
+
+
+ Bases: BaseModel
Allows a service to suggest a set of changes in the context of the current activity +(e.g. changing the dose of a medication currently being prescribed, for the order-sign activity). +If suggestions are present, selectionBehavior MUST also be provided.
+https://cds-hooks.org/specification/current/#suggestion
+ + + + + + +healthchain/models/responses/cdsresponse.pyClinicalDocumentation
+
+
+
+ Bases: BaseUseCase
Implements EHR backend strategy for clinical documentation (NoteReader)
+This class represents the backend strategy for clinical documentation using the NoteReader system.
+It inherits from the BaseUseCase class and provides methods for processing NoteReader documents.
| ATTRIBUTE | +DESCRIPTION | +
|---|---|
service_api |
+
+
+
+ The service API method to be used for processing the documents. +
+
+ TYPE:
+ |
+
service_config |
+
+
+
+ The configuration for the service. +
+
+ TYPE:
+ |
+
service |
+
+
+
+ The service to be used for processing the documents. +
+
+ TYPE:
+ |
+
client |
+
+
+
+ The client to be used for communication with the service. +
+
+ TYPE:
+ |
+
overwrite |
+
+
+
+ Whether to overwrite existing data in the CDA document. +
+
+ TYPE:
+ |
+
healthchain/use_cases/clindoc.py84 + 85 + 86 + 87 + 88 + 89 + 90 + 91 + 92 + 93 + 94 + 95 + 96 + 97 + 98 + 99 +100 +101 +102 +103 +104 +105 +106 +107 +108 +109 +110 +111 +112 +113 +114 +115 +116 +117 +118 +119 +120 +121 +122 +123 +124 +125 +126 +127 +128 +129 +130 +131 +132 +133 +134 +135 +136 +137 +138 +139 +140 +141 +142 +143 +144 +145 +146 +147 +148 +149 +150 +151 +152 +153 +154 +155 +156 +157 +158 +159 +160 +161 +162 +163 +164 +165 +166 +167 +168 +169 +170 +171 +172 +173 +174 +175 +176 +177 +178 +179 +180 +181 +182 +183 +184 +185 +186 +187 +188 +189 +190 +191 +192 +193 +194 +195 +196 +197 | |
process_notereader_document(request)
+
+Process the NoteReader document using the configured service API.
+This method handles the execution of the NoteReader service. It validates the +service configuration, checks the input parameters, executes the service +function, and ensures the correct response type is returned.
+ + +| PARAMETER | +DESCRIPTION | +
|---|---|
+ request
+ |
+
+
+
+ The request object containing the CDA document to be processed. +
+
+ TYPE:
+ |
+
| RETURNS | +DESCRIPTION | +
|---|---|
+ CdaResponse
+ |
+
+
+
+ The response object containing the processed CDA document. +
+
+ TYPE:
+ |
+
| RAISES | +DESCRIPTION | +
|---|---|
+
+ AssertionError
+
+ |
+
+
+
+ If the service function is not properly configured. + |
+
+
+ TypeError
+
+ |
+
+
+
+ If the output type does not match the expected CdaResponse type. + |
+
This method performs several checks to ensure the integrity of the service: +1. Verifies that the service API is configured. +2. Validates the signature of the service function. +3. Ensures the service function accepts a CdaRequest as its argument. +4. Verifies that the service function returns a CdaResponse.
+healthchain/use_cases/clindoc.pyClinicalDocumentationStrategy
+
+
+
+ Bases: BaseStrategy
Handles the request construction and validation of a NoteReader CDA file
+ + + + + + +healthchain/use_cases/clindoc.pyconstruct_cda_xml_document()
+
+This function should wrap FHIR data from CcdFhirData into a template CDA file (dep. vendor +TODO: implement this function
+ + +construct_request(data, workflow)
+
+Constructs a CDA request for clinical documentation use cases (NoteReader)
+ + +| PARAMETER | +DESCRIPTION | +
|---|---|
+ data
+ |
+
+
+
+ CDA data to be injected in the request +
+
+ TYPE:
+ |
+
+ workflow
+ |
+
+
+
+ The NoteReader workflow type, e.g. notereader-sign-inpatient +
+
+ TYPE:
+ |
+
| RETURNS | +DESCRIPTION | +
|---|---|
+ CdaRequest
+ |
+
+
+
+ A Pydantic model that wraps CDA data for SOAP request +
+
+ TYPE:
+ |
+
| RAISES | +DESCRIPTION | +
|---|---|
+
+ ValueError
+
+ |
+
+
+
+ If the workflow is invalid or the data does not validate properly. + |
+
healthchain/use_cases/clindoc.pyCdaRequest
+
+
+
+ Bases: BaseModel
healthchain/models/requests/cdarequest.pyfrom_dict(data)
+
+
+ classmethod
+
+
+model_dump(*args, **kwargs)
+
+model_dump_xml(*args, **kwargs)
+
+Decodes and dumps document as an xml string
+ +healthchain/models/requests/cdarequest.pyCdaResponse
+
+
+
+ Bases: BaseModel
healthchain/models/responses/cdaresponse.pyfrom_dict(data)
+
+
+ classmethod
+
+
+model_dump(*args, **kwargs)
+
+model_dump_xml(*args, **kwargs)
+
+Decodes and dumps document as an xml string
+ +healthchain/models/responses/cdaresponse.py