Updated meds_reader to 0.1.9

pyproject.toml

@@ -34,7 +34,7 @@ dependencies = [
     "femr==0.2.0",
     "Jinja2==3.1.3",
     "meds==0.3.3",
-    "meds_reader==0.1.1",
+    "meds_reader==0.1.9",
     "networkx==3.2.1",
     "numpy==1.24.3",
     "packaging==23.2",

src/cehrbert/data_generators/hf_data_generator/hf_dataset.py

@@ -2,14 +2,14 @@
 
 from datasets import Dataset, DatasetDict, IterableDataset, IterableDatasetDict
 
-from ...data_generators.hf_data_generator.hf_dataset_mapping import (
+from cehrbert.data_generators.hf_data_generator.hf_dataset_mapping import (
     DatasetMapping,
     HFFineTuningMapping,
     HFTokenizationMapping,
     SortPatientSequenceMapping,
 )
-from ...models.hf_models.tokenization_hf_cehrbert import CehrBertTokenizer
-from ...runners.hf_runner_argument_dataclass import DataTrainingArguments
+from cehrbert.models.hf_models.tokenization_hf_cehrbert import CehrBertTokenizer
+from cehrbert.runners.hf_runner_argument_dataclass import DataTrainingArguments
 
 CEHRBERT_COLUMNS = [
     "concept_ids",

src/cehrbert/data_generators/hf_data_generator/meds_to_cehrbert_conversion_rules/meds_to_cehrbert_micmic4.py

@@ -1,7 +1,7 @@
 import re
 from typing import List
 
-from ....data_generators.hf_data_generator.meds_to_cehrbert_conversion_rules.meds_to_cehrbert_base import (
+from cehrbert.data_generators.hf_data_generator.meds_to_cehrbert_conversion_rules.meds_to_cehrbert_base import (
     EventConversionRule,
     MedsToCehrBertConversion,
 )

src/cehrbert/data_generators/hf_data_generator/meds_utils.py

@@ -10,13 +10,13 @@
 import pandas as pd
 from datasets import Dataset, DatasetDict, Split
 
-from ...data_generators.hf_data_generator.hf_dataset import apply_cehrbert_dataset_mapping
-from ...data_generators.hf_data_generator.hf_dataset_mapping import MedToCehrBertDatasetMapping, birth_codes
-from ...data_generators.hf_data_generator.meds_to_cehrbert_conversion_rules.meds_to_cehrbert_base import (
+from cehrbert.data_generators.hf_data_generator.hf_dataset import apply_cehrbert_dataset_mapping
+from cehrbert.data_generators.hf_data_generator.hf_dataset_mapping import MedToCehrBertDatasetMapping, birth_codes
+from cehrbert.data_generators.hf_data_generator.meds_to_cehrbert_conversion_rules.meds_to_cehrbert_base import (
     MedsToCehrBertConversion,
 )
-from ...med_extension.schema_extension import CehrBertPatient, Event, Visit
-from ...runners.hf_runner_argument_dataclass import DataTrainingArguments, MedsToCehrBertConversionType
+from cehrbert.med_extension.schema_extension import CehrBertPatient, Event, Visit
+from cehrbert.runners.hf_runner_argument_dataclass import DataTrainingArguments, MedsToCehrBertConversionType
 
 UNKNOWN_VALUE = "Unknown"
 DEFAULT_ED_CONCEPT_ID = "9203"
@@ -39,19 +39,48 @@ def get_meds_to_cehrbert_conversion_cls(
     raise RuntimeError(f"{meds_to_cehrbert_conversion_type} is not a valid MedsToCehrBertConversionType")
 
 
-def get_patient_split(meds_reader_db_path: str) -> Dict[str, List[int]]:
-    patient_split = pd.read_parquet(os.path.join(meds_reader_db_path, "metadata/patient_splits.parquet"))
-    result = {str(group): records["patient_id"].tolist() for group, records in patient_split.groupby("split")}
+def get_subject_split(meds_reader_db_path: str) -> Dict[str, List[int]]:
+    patient_split = pd.read_parquet(os.path.join(meds_reader_db_path, "metadata/subject_splits.parquet"))
+    result = {str(group): records["subject_id"].tolist() for group, records in patient_split.groupby("split")}
     return result
 
 
 class PatientBlock:
+    """
+    Represents a block of medical events for a single patient visit, including.
+
+    inferred visit type and various admission and discharge statuses.
+
+    Attributes:
+        visit_id (int): The unique ID of the visit.
+        events (List[meds_reader.Event]): A list of medical events associated with this visit.
+        min_time (datetime): The earliest event time in the visit.
+        max_time (datetime): The latest event time in the visit.
+        conversion (MedsToCehrBertConversion): Conversion object for mapping event codes to CEHR-BERT.
+        has_ed_admission (bool): Whether the visit includes an emergency department (ED) admission event.
+        has_admission (bool): Whether the visit includes an admission event.
+        has_discharge (bool): Whether the visit includes a discharge event.
+        visit_type (str): The inferred type of visit, such as inpatient, ED, or outpatient.
+    """
+
     def __init__(
         self,
         events: List[meds_reader.Event],
         visit_id: int,
         conversion: MedsToCehrBertConversion,
     ):
+        """
+        Initializes a PatientBlock instance, inferring the visit type based on the events and caching.
+
+        admission and discharge status.
+
+        Args:
+            events (List[meds_reader.Event]): The medical events associated with the visit.
+            visit_id (int): The unique ID of the visit.
+            conversion (MedsToCehrBertConversion): Conversion object for mapping event codes to CEHR-BERT.
+
+        Attributes are initialized to store visit metadata and calculate admission/discharge statuses.
+        """
         self.visit_id = visit_id
         self.events = events
         self.min_time = events[0].time
@@ -73,28 +102,51 @@ def __init__(
             self.visit_type = DEFAULT_OUTPATIENT_CONCEPT_ID
 
     def _has_ed_admission(self) -> bool:
-        """Make this configurable in the future."""
+        """
+        Determines if the visit includes an emergency department (ED) admission event.
+
+        Returns:
+            bool: True if an ED admission event is found, False otherwise.
+        """
         for event in self.events:
             for matching_rule in self.conversion.get_ed_admission_matching_rules():
                 if re.match(matching_rule, event.code):
                     return True
         return False
 
     def _has_admission(self) -> bool:
+        """
+        Determines if the visit includes a hospital admission event.
+
+        Returns:
+            bool: True if an admission event is found, False otherwise.
+        """
         for event in self.events:
             for matching_rule in self.conversion.get_admission_matching_rules():
                 if re.match(matching_rule, event.code):
                     return True
         return False
 
     def _has_discharge(self) -> bool:
+        """
+        Determines if the visit includes a discharge event.
+
+        Returns:
+            bool: True if a discharge event is found, False otherwise.
+        """
         for event in self.events:
             for matching_rule in self.conversion.get_discharge_matching_rules():
                 if re.match(matching_rule, event.code):
                     return True
         return False
 
     def get_discharge_facility(self) -> Optional[str]:
+        """
+        Extracts the discharge facility code from the discharge event, if present.
+
+        Returns:
+            Optional[str]: The sanitized discharge facility code, or None if no discharge event is found.
+        """
         if self._has_discharge():
             for event in self.events:
                 for matching_rule in self.conversion.get_discharge_matching_rules():
@@ -105,12 +157,22 @@ def get_discharge_facility(self) -> Optional[str]:
         return None
 
     def _convert_event(self, event) -> List[Event]:
+        """
+        Converts a medical event into a list of CEHR-BERT-compatible events, potentially parsing.
+
+        numeric values from text-based events.
+
+        Args:
+            event (meds_reader.Event): The medical event to be converted.
+
+        Returns:
+            List[Event]: A list of converted events, possibly numeric, based on the original event's code and value.
+        """
         code = event.code
         time = getattr(event, "time", None)
         text_value = getattr(event, "text_value", None)
         numeric_value = getattr(event, "numeric_value", None)
-        # We try to parse the numeric values from the text value, in other words,
-        # we try to construct numeric events from the event with a text value
+
         if numeric_value is None and text_value is not None:
             conversion_rule = self.conversion.get_text_event_to_numeric_events_rule(code)
             if conversion_rule:
@@ -140,14 +202,20 @@ def _convert_event(self, event) -> List[Event]:
         ]
 
     def get_meds_events(self) -> Iterable[Event]:
+        """
+        Retrieves all medication events for the visit, converting each raw event if necessary.
+
+        Returns:
+            Iterable[Event]: A list of CEHR-BERT-compatible medication events for the visit.
+        """
         events = []
         for e in self.events:
             events.extend(self._convert_event(e))
         return events
 
 
 def convert_one_patient(
-    patient: meds_reader.Patient,
+    patient: meds_reader.Subject,
     conversion: MedsToCehrBertConversion,
     default_visit_id: int = 1,
     prediction_time: datetime = None,
@@ -296,10 +364,10 @@ def convert_one_patient(
             age_at_index -= 1
 
     # birth_datetime can not be None
-    assert birth_datetime is not None, f"patient_id: {patient.patient_id} does not have a valid birth_datetime"
+    assert birth_datetime is not None, f"patient_id: {patient.subject_id} does not have a valid birth_datetime"
 
     return CehrBertPatient(
-        patient_id=patient.patient_id,
+        patient_id=patient.subject_id,
         birth_datetime=birth_datetime,
         visits=visits,
         race=race if race else UNKNOWN_VALUE,
@@ -346,7 +414,7 @@ def _meds_to_cehrbert_generator(
 ) -> CehrBertPatient:
     conversion = get_meds_to_cehrbert_conversion_cls(meds_to_cehrbert_conversion_type)
     for shard in shards:
-        with meds_reader.PatientDatabase(path_to_db) as patient_database:
+        with meds_reader.SubjectDatabase(path_to_db) as patient_database:
             for patient_id, prediction_time, label in shard:
                 patient = patient_database[patient_id]
                 yield convert_one_patient(patient, conversion, default_visit_id, prediction_time, label)
@@ -363,20 +431,21 @@ def _create_cehrbert_data_from_meds(
     if data_args.cohort_folder:
         cohort = pd.read_parquet(os.path.join(data_args.cohort_folder, split))
         for cohort_row in cohort.itertuples():
-            patient_id = cohort_row.patient_id
+            subject_id = cohort_row.subject_id
             prediction_time = cohort_row.prediction_time
             label = int(cohort_row.boolean_value)
-            batches.append((patient_id, prediction_time, label))
+            batches.append((subject_id, prediction_time, label))
     else:
-        patient_split = get_patient_split(data_args.data_folder)
-        for patient_id in patient_split[split]:
-            batches.append((patient_id, None, None))
+        patient_split = get_subject_split(data_args.data_folder)
+        for subject_id in patient_split[split]:
+            batches.append((subject_id, None, None))
 
     split_batches = np.array_split(np.asarray(batches), data_args.preprocessing_num_workers)
     batch_func = functools.partial(
         _meds_to_cehrbert_generator,
         path_to_db=data_args.data_folder,
         default_visit_id=default_visit_id,
+        meds_to_cehrbert_conversion_type=data_args.meds_to_cehrbert_conversion_type,
     )
     dataset = Dataset.from_generator(
         batch_func,

src/cehrbert/runners/hf_cehrbert_pretrain_runner.py

@@ -179,7 +179,7 @@ def main():
                 dataset = load_from_disk(meds_extension_path)
                 if data_args.streaming:
                     dataset = dataset.to_iterable_dataset(num_shards=training_args.dataloader_num_workers)
-            except RuntimeError as e:
+            except FileNotFoundError as e:
                 LOG.exception(e)
                 dataset = create_dataset_from_meds_reader(data_args, is_pretraining=True)
                 if not data_args.streaming:

src/cehrbert/spark_apps/decorators/patient_event_decorator.py

@@ -16,7 +16,7 @@ class AttType(Enum):
     DAY = "day"
     WEEK = "week"
     MONTH = "month"
-    CEHR_BERT = "cehrbert"
+    CEHR_BERT = "cehr_bert"
     MIX = "mix"
     NONE = "none"