| Literature DB >> 35308962 |
Hannah Eyre1,2, Alec B Chapman1,2, Kelly S Peterson2,3, Jianlin Shi2, Patrick R Alba1,2, Makoto M Jones1,2, Tamára L Box3, Scott L DuVall1,2, Olga V Patterson1,2.
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. ©2021 AMIA - All rights reserved.Entities:
Mesh：
Year: 2022 PMID： 35308962 PMCID： PMC8861690
Source DB: PubMed Journal: AMIA Annu Symp Proc ISSN： 1559-4076