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

AMIA ... Annual Symposium proceedings, 2021, Vol.2021, p.438-447 [Peer Reviewed Journal]

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Title:
Launching into clinical space with medspaCy: a new clinical text processing toolkit in Python
Author: 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
Subjects: Algorithms ; Humans ; Machine Learning ; Natural Language Processing
Is Part Of: AMIA ... Annual Symposium proceedings, 2021, Vol.2021, p.438-447
Description: 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.
Publisher: United States: American Medical Informatics Association
Language: English
Identifier: EISSN: 1942-597X
PMID: 35308962
Source: GFMER Free Medical Journals
MEDLINE
PubMed Central

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