NLP Pipeline for Medical Data Processing
Built an NLP pipeline to process Medline XML and ChEBI ontology data for clinical research and pharmaceutical applications.
Project Overview
Developed a comprehensive NLP pipeline as part of my Master's coursework to process medical literature and chemical entity data. The system uses spaCy for Named Entity Recognition (NER) and Whoosh for efficient text indexing and search. The pipeline processes Medline XML data and integrates ChEBI (Chemical Entities of Biological Interest) ontology for standardized chemical entity recognition. This project enhanced my understanding of biomedical NLP challenges, including handling large-scale medical datasets, entity disambiguation, and ontology integration. The modular design allows for processing various types of medical literature and demonstrates practical applications of NLP techniques in healthcare and pharmaceutical research contexts.
Key Features
- ✓Medline XML data processing
- ✓ChEBI ontology integration
- ✓Named Entity Recognition with spaCy
- ✓Fast entity resolution with Whoosh
- ✓Chemical entity recognition optimization
- ✓Clinical research data support
- ✓Pharmaceutical application compatibility
- ✓Scalable pipeline architecture
Technical Challenges
- ⚡Processing large medical datasets efficiently
- ⚡Integrating multiple data sources
- ⚡Optimizing entity recognition accuracy
- ⚡Building scalable NLP pipeline
Technologies Used
Project Info
Collaboration
Team
Lead Developer & Researcher
Screenshots
