Clinical NLP Pipeline

A natural language processing pipeline for clinical text analysis and healthcare data processing.

Features

• Clinical text preprocessing and validation
• Named Entity Recognition (NER) with hybrid models
• Topic modeling and clustering
• Medical concept mapping
• Risk scoring algorithms
• Negation detection
• Text summarization
• CPT code extraction and mapping
• Interactive Streamlit web interface
• Docker containerization support

Installation

poetry install

Usage

Run the Streamlit app:

streamlit run streamlit_app.py

Or use Docker:

docker-compose up

Data Requirements

This project requires clinical text data for NLP processing. See data/README.md for detailed instructions.

Quick Start with Sample Data

Sample data is provided in data/sample/ for immediate testing.

Production Data

For production use, obtain access to the MIMIC-III Clinical Database:

1. Request access at https://physionet.org/content/mimiciii/
2. Complete required training and sign data use agreement
3. Download and place data in data/mimic-iii-clinical-database-demo-1.4/

Important: Never commit real clinical data. All patient data should remain local only.

Development

Run tests:

pytest

Future Scope

• Apache Airflow orchestration for batch processing
• Real-time streaming with Kafka integration
• Advanced ML model deployment with MLflow
• FHIR data integration
• Multi-language support for clinical texts
• Advanced privacy-preserving techniques (differential privacy)
• Integration with EHR systems
• Scalable distributed processing with Spark
• Model monitoring and drift detection

Requirements

• Python 3.10+
• Poetry
• Docker (optional)