Launching into clinical space with medspaCy: a new clinical text processing toolkit in Python

Eyre, Hannah; Chapman, Alec B; Peterson, Kelly S; Shi, Jianlin; Alba, Patrick R; Jones, Makoto M; Box, Tamara L; DuVall, Scott L; System, Olga V Patterson VA Salt Lake City Health Care; Utah, University of; City, Salt Lake; UT,; USA,; Analytics, Veterans Health Administration Office of; Integration, Performance

Despite impressive success of machine learning algorithms in clinical natural language processing (cNLP), rule-based approaches still have a prominent role. In this paper, we introduce medspaCy, an extensible, open-source cNLP library based on spaCy framework that allows flexible integration of rule-based and machine learning-based algorithms adapted to clinical text. MedspaCy includes a variety of components that meet common cNLP needs such as context analysis and mapping to standard terminologies. By utilizing spaCy's clear and easy-to-use conventions, medspaCy enables development of custom pipelines that integrate easily with other spaCy-based modules. Our toolkit includes several core components and facilitates rapid development of pipelines for clinical text.