Healthcare Report Summarizer Project Description This project is a lightweight, offline-friendly tool that automates the summarization of raw medical reports. It takes an unstructured medical report (PDF or text) as input and generates a concise, patient-friendly summary. The goal is to demystify complex medical terminology, making it easier for patients to understand their diagnosis, key findings, and recommended next steps, and for doctors to quickly review key information.
Features PDF & Text Extraction: Seamlessly extracts text from various medical report formats using PyMuPDF and pdfplumber.
NLP-Powered Summarization: Utilizes a pre-trained Hugging Face transformer model to generate accurate and relevant summaries.
Medical Term Simplification: Includes a custom layer to simplify complex medical jargon (e.g., "hypertension" to "high blood pressure") for enhanced readability.
Interactive UI: A user-friendly interface built with Streamlit allows for easy report uploads and side-by-side comparison of the original text and the generated summary.
Offline Capability: The entire application runs locally, ensuring data privacy and security without needing an internet connection.
Why this Project is Valuable Demonstrates Advanced NLP Skills: Showcases proficiency in text processing, information extraction, and abstractive summarization.
Solves a Real-World Problem: Addresses a critical communication gap in the healthcare industry, highlighting the practical impact of AI.
Relevant for the AI/Healthcare Domain: Positions the developer as a candidate with a strong understanding of a high-growth, high-impact field.
How to Use Prerequisites Python 3.x
A C++ compiler (for some dependencies, especially on Windows). See this guide for help with installation.
Installation Clone the repository:
Bash
git clone https://github.com/your-username/health-report-summarizer.git cd health-report-summarizer Set up a virtual environment:
Bash
python -m venv .venv
.venv\Scripts\activate
source .venv/bin/activate Install dependencies:
Bash
pip install -r requirements.txt
Running the Application Bash
streamlit run app.py
Project Roadmap
Phase 1: Setup & Data
Phase 2: PDF to Text
Phase 3: Summarization Model
Phase 4: Healthcare Optimization
Phase 5: User Interface