1. Launching into clinical space with medspaCy: a new clinical text processing toolkit in Python
- Author
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Eyre, Hannah, Chapman, Alec B, Peterson, Kelly S, Shi, Jianlin, Alba, Patrick R, Jones, Makoto M, Box, Tamara L, DuVall, Scott L, and Patterson, Olga V
- Subjects
Computer Science - Computation and Language - Abstract
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., Comment: Accepted to AMIA Annual Symposium 2021
- Published
- 2021