Intelligent Medical Prescription System

Project Goal: To create a Python application that can automate the process of updating and generating medical prescriptions using machine learning techniques.

Core Functionalities:
- Prescription Data Handling:
- Data Input: The system should read patient data from previous prescriptions, including diabetes level, blood pressure, SPO2 level, etc.
- Data Update: The system must update these parameters in the current prescription.
- Voice-to-Text Conversion and Data Extraction:
- Voice Input: The system needs to accept voice input to capture new parameter values.
- Speech Recognition: Convert the voice input into text using speech recognition technology.
- Data Extraction: Extract parameter values from the converted text using Natural Language Processing (NLP).
- Intelligent Placement: Ensure extracted values are correctly placed in the new medical prescription regardless of spoken order.
- Machine Learning Implementation:
- Employ machine learning algorithms for disease prediction using patient data, utilizing libraries like TensorFlow, Pytorch, and Scikit-Learn.
- Electronic Health Records (EHR): Develop a system that aids healthcare providers in accessing, updating, and analyzing patient records; extract and analyze EHR data to enhance patient care.