Project MONAI (Medical Open Network for AI) is an open-source framework that accelerates research and clMedSpaCy is a powerful library built specifically for clinical natural language processing (NLP) using the popular SpaCy framework. Designed to handle the unique complexities of clinical text, MedSpaCy provides specialized tools that allow healthcare providers and researchers to analyze medical data more efficiently. With its modular design, users can integrate only the tools they need, including sentence segmentation, clinical concept extraction, attribute assertion, and section detection. This flexibility makes MedSpaCy an excellent resource for those looking to leverage clinical data for research or patient care improvement.
Each module in MedSpaCy addresses a particular need within clinical text processing:
- Preprocess: Prepares clinical text by modifying it for better processing.
- Sentence Splitter: Segments clinical sentences.
- NER (Named Entity Recognition): Extracts key clinical concepts.
- Context: Detects semantic modifiers like negation and uncertainty.
- Section Detection: Identifies and segments specific clinical sections.
- Postprocess: Provides options to modify or remove extracted data.
- Visualization: Visualizes extracted entities and relationships.
- QuickUMLS Integration: A unique component for extracting UMLS concepts.
Since MedSpaCy builds on SpaCy’s core, users can apply it as a seamless part of a SpaCy pipeline, adding critical clinical NLP capabilities. It’s in active development, with support for future enhancements like clinical model training and relation extraction, making it a valuable tool for the evolving field of clinical NLP.
