Using Language Models for Generating Patient Clinical Letters

Overview

This project explores the feasibility of using language models (LMs) to generate patient clinical letters while optimizing computational efficiency and ensuring data privacy.

Objectives

• Automate Clinical Letter Generation: Evaluate LMs' capability to produce accurate and coherent clinical letters.
• Reduce Computational Requirements: Implement optimization techniques or lightweight models to improve efficiency.
• Ensure Data Privacy: Apply privacy-preserving methods to comply with healthcare regulations and protect patient data.
• Enhance Workflow Efficiency: Streamline clinical documentation for healthcare professionals.

Features

• 📄 Automated Letter Drafting – Generate structured clinical letters efficiently.
• 🔒 Privacy-Focused – Ensure compliance with data protection standards (e.g., HIPAA, GDPR).
• ⚡ Optimized for Performance – Reduce computational overhead without sacrificing accuracy.
• 🏥 Healthcare Integration – Adaptable for electronic health record (EHR) systems.

Installation

Prerequisites

Setup

1. Clone the repository

   git [clone https://github.com/your-repo/clinical-letter-lm.git](https://github.com/cepdnaclk/e19-4yp-Using-LMs-to-Write-Patient-Clinical-Letters.git)
   cd e19-4yp-Using-LMs-to-Write-Patient-Clinical-Letters