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. 2022 Feb 21:2021:438-447.
eCollection 2021.

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

Hannah Eyre  1   2 , Alec B Chapman  1   2 , Kelly S Peterson  2   3 , Jianlin Shi  2 , Patrick R Alba  1   2 , Makoto M Jones  1   2 , Tamára L Box  3 , Scott L DuVall  1   2 , Olga V Patterson  1   2
Affiliations

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

Hannah Eyre et al. AMIA Annu Symp Proc. .

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.

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Figures

Figure 1:
Figure 1:
Overview of medspaCy architecture.
Figure 2:
Figure 2:
Example of a text processing pipeline that utilizes medspaCy and other spaCy-based components.
Figure 3:
Figure 3:
Example of visualization using highlight and context arrows.
Figure 4:
Figure 4:
Highlighted section headers delineate template questions with the section body as the responses when processing semi-structured text.
See this image and copyright information in PMC

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