Traditional healthcare services often adopt a one-size-fits-all approach with limited medical support available only during infrequent appointments scheduled months apart. Additionally, studies have been done to substantiate the need for good follow-up care with timely access to medical conditions and information could lead to better recoveries. Thereby, we aim to create an AI-Powered Health Condition Monitoring and Nutrition Planning system to enable a tailored health journey catering uniquely to the User’s needs and provide a wealth of personalised resources to accompany the User throughout the recovery journey.
| Official Full Name | Student ID (MTech Applicable) | Work Items (Who Did What) | |
|---|---|---|---|
| Yatharth Mahesh Sant | A0286001R | Completed the code for NLP chatbot, multi label classification and the backend for disease diagnosis | [email protected] |
| Kristofer Roos | A0285949A | Facilitated data integration and transformation for disease detection and meal planning; research, interviews, and data sourcing; risk analysis; developed unique selling/value propositions. | [email protected] |
| Chua Kian Yong Kenny | A0056377W | Data acquisition, data preprocessing, backend for meal planner | [email protected] |
| Zhang Yusen | A0285839H | Designed and implemented web frontend with user interface. Completed the backend for meal planner and front-back integration. | [email protected] |
| Hao Zhenmao | A0285960R | Completed the Telegram notification feature. Overall structural designing and testing of the system code. | [email protected] |
Refer to appendix <Installation & User Guide> in project report at Github Folder: ProjectReport
Refer to the README.md file in ProjectCode for installation on local machine.
Refer to project report at Github Folder: ProjectReport
Sections of Project Report:
- Executive Summary
- Business Case and Market Research
- System Design and Model
- System Development and Implementation
- Risk Analysis and Next steps
- Conclusions
- Appendix of report: Project Proposal
- Appendix of report: Mapped System Functionalities against knowledge, techniques and skills of modular courses: MR, RS, CGS
- Appendix of report: Installation and User Guide
- Appendix of report: 1-2 pages individual project report per project member, including: Individual reflection of project journey: (1) personal contribution to group project (2) what learnt is most useful for you (3) how you can apply the knowledge and skills in other situations or your workplaces
- Appendix of report: References
This Machine Reasoning (MR) course is part of the Analytics and Intelligent Systems and Graduate Certificate in Intelligent Reasoning Systems (IRS) series offered by NUS-ISS.
Lecturer: GU Zhan (Sam)