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Medical Transcript Analyzer

A Flask-based web application that processes medical conversation transcripts and generates structured medical reports using Natural Language Processing (NLP).

Overview

The Medical Transcript Analyzer extracts key medical information from doctor-patient conversation transcripts and organizes it into a structured format. The application uses spaCy for NLP processing and pattern matching to identify important medical entities and context.

Features

• Upload and process medical conversation transcripts
• Extract patient information, symptoms, diagnosis, treatment details, and prognosis
• Generate structured medical reports with key clinical information
• Identify important medical phrases and keywords from conversations
• Export results in JSON or text format

How It Works

1. The application processes raw medical transcripts using spaCy NLP
2. Pattern matching and keyword extraction identify medical entities and context
3. Information is organized into a structured medical report format
4. Results are displayed in an easy-to-read interface

Output

The application generates a comprehensive medical report with the following sections:

Patient Information:

Treatment, Status, and Medical Phrases:

Technical Details

• Backend: Python Flask application with spaCy NLP
• NLP Processing: Entity recognition, pattern matching, and contextual analysis
• Data Structure: JSON-formatted medical reports
• Deployment: Includes pickle support for model portability

Implementation

The core functionality is implemented in three main functions:

1. process_medical_transcript(): Extracts structured information from text
2. extract_keywords(): Identifies important medical phrases and keywords
3. generate_medical_summary(): Combines structured data and keywords into a complete report

Installation and Usage

1. Clone the repository
2. Install dependencies: pip install flask spacy
3. Download spaCy model: python -m spacy download en_core_web_sm
4. Run the application: python app.py
5. Access the web interface at http://localhost:5000

Deployment

For deployment, the application includes functionality to save and load the spaCy model as a pickle file, making it easier to deploy in environments where installing models might be restricted.