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README.md

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+# Medical Record Synthesis using GPT-3.5
+# Author Dr.Nyein Chan Ko Ko (2023)
+
+## Overview
+This project leverages GPT-3.5 to synthesize medical records, aiming to provide a compliant solution for researchers needing access to patient data while respecting privacy laws like HIPAA and GDPR. 
+
+## Repository Contents
+- `main.py`: Main function execution
+- `data_generation.py`: Synthetic patient profile and record generation
+- `gpt_processing.py`: GPT model utilization for record creation
+- `file_management.py`: Transformation of GPT texts into CSV
+- `required_libraries.py`: Library imports
+
+## Objectives
+- Evaluate synthetic medical record generation feasibility and accuracy
+- Process analysis from prompt engineering to database-ready notes
+- Apply findings in language model research and prototyping
+
+## Methodology
+- **Prompt Engineering**: Develop prompts for GPT-3.5 to accurately generate medical data
+- **Data Generation**: Create patient profiles and detailed medical records
+- **Data Processing**: Convert generated texts to structured CSV format
+
+## Observations
+- Generated 1079 balanced patient records by nationality and gender
+- Noted realistic age distributions and disease prevalences
+- Addressed potential biases and the importance of reliable data sources
+
+## Applications
+- Enhancing NLP tasks in healthcare
+- Supporting medical education and research
+- Prototype testing within privacy regulations
+
+## Conclusion
+The project highlights the potential of GPT-3.5 in synthesizing realistic medical records, providing a valuable tool for medical research within ethical and legal standards.
+
+## Usage
+Install dependencies listed in `required_libraries.py`. Run `main.py` to begin synthetic medical record generation. Follow component-specific instructions for detailed operations.