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

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...

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Published inAMIA ... Annual Symposium proceedings Vol. 2021; pp. 438 - 447
Main Authors Eyre, Hannah, Chapman, Alec B, Peterson, Kelly S, Shi, Jianlin, Alba, Patrick R, Jones, Makoto M, Box, Tamára L, DuVall, Scott L, Patterson, Olga V
Format Journal Article
LanguageEnglish
Published United States American Medical Informatics Association 2021
Subjects
Algorithms
Humans
Machine Learning
Natural Language Processing
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Summary: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.
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ISSN:1942-597X
1559-4076