Personalized-Medication-Recommendation-System

MediPredictor by KOSASIH is licensed under Creative Commons Attribution 4.0 International

Personalized-Medication-Recommendation-System

The Personalized Medication Recommendation System is an AI-driven application that provides tailored medication suggestions for patients based on their unique health profiles. By integrating with Electronic Health Records (EHR) using the SMART on FHIR standard, this system analyzes patient data—including demographics, medical history, and current medications—to deliver accurate and personalized recommendations. Utilizing advanced machine learning algorithms, the app aims to assist healthcare providers in making informed medication decisions, improving patient outcomes, and enhancing overall treatment efficacy. The repository includes the complete codebase, model training scripts, and documentation for deployment and usage.

Personalized Medication Recommendation System

Overview

The Personalized Medication Recommendation System is an AI-driven application designed to provide tailored medication suggestions for patients based on their unique health profiles. By integrating with Electronic Health Records (EHR) using the SMART on FHIR standard, this system analyzes patient data—including demographics, medical history, and current medications—to deliver accurate and personalized recommendations. The application aims to assist healthcare providers in making informed medication decisions, improving patient outcomes, and enhancing overall treatment efficacy.

Features

Personalized Medication Recommendations: Provides tailored medication suggestions based on patient data.
EHR Integration: Seamlessly integrates with EHR systems using the SMART on FHIR standard.
User Feedback: Allows healthcare providers to submit feedback on recommendations.
Sentiment Analysis: Analyzes patient reviews to gauge sentiment towards medications.
User -Friendly Interface: Intuitive and responsive design for easy navigation.

Tech Stack

Backend: Flask, FHIR Client, Scikit-learn, Joblib, Pandas, NumPy
Frontend: React, React Router
Database: PostgreSQL or MongoDB (if applicable)
Deployment: Docker (optional), Heroku, AWS, or any cloud service

Getting Started

Prerequisites

Python 3.x
Node.js and npm
PostgreSQL or MongoDB (if applicable)

Backend Setup

Clone the repository:

git clone https://github.com/KOSASIH/Personalized-Medication-Recommendation-System.git
cd Personalized-Medication-Recommendation-System/backend

Create a virtual environment:

python -m venv venv
source venv/bin/activate  # On Windows use `venv\Scripts\activate`

Install the required Python packages:
```
pip install -r requirements.txt
```
Configure the application settings in config.py:
- Set your FHIR app ID and API base URL.
- Adjust any other settings as needed.
Run the backend server:
```
python run.py
```

Frontend Setup

Navigate to the frontend directory:
```
cd ../frontend
```
Install the required Node.js packages:
```
npm install
```
Start the frontend application:
```
npm start
```
Open your browser and go to http://localhost:3000.

Usage

Access the Application: Open your web browser and navigate to http://localhost:3000.
View Recommendations: The home page will display personalized medication recommendations based on the patient data fetched from the backend.
Submit Feedback: Use the feedback form to submit your thoughts on the recommendations provided.

API Endpoints

Recommendations

Endpoint: /api/recommendations
Method: POST
Request Body:
```
  {
      "patient_id": "string"
  }
```

Response:

  [
      {
          "medication": "string",
          "reason": "string",
          "dosage": "string"
      }
  ]

Feedback

Endpoint: /api/feedback
Method: POST

Request Body:

  {
      "patient_id": "string",
      "feedback": "string"
  }

Response:

  {
      "message": "Feedback submitted successfully"
  }

Sentiment Analysis

Endpoint: /api/sentiment
Method: POST
Request Body:
```
  {
      "review_text": "string"
  }
```

Response:

  {
      "sentiment": "positive" | "negative"
  }

Future Enhancements

Integration of real-time patient monitoring data to refine recommendations.
Expansion of the model to include additional therapeutic areas and medication classes.
Development of a mobile application for on-the-go access to recommendations and patient data.

Contributing

Contributions are welcome! Please feel free to submit a pull request or open an issue for any suggestions or improvements.

License

This project is licensed under the MIT License. See the LICENSE file for details.

Acknowledgments

Flask
React
SMART on FHIR for providing a framework for integrating with EHR systems.
Scikit-learn for machine learning capabilities.
PostgreSQL or MongoDB for database management.
All contributors and supporters of the project.

Contact

For any inquiries or feedback, please reach out to the project maintainer at [kosasihg88@gmail.com].

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