Launching into Clinical Space with MedspaCy: a New Clinical Text Processing Toolkit in Python

Overview

Journal AMIA Annu Symp Proc

Specialty Medical Informatics

Date 2022 Mar 21

PMID 35308962

Authors

Hannah Eyre

Alec B Chapman

Kelly S Peterson

Jianlin Shi

Patrick R Alba

Makoto M Jones

Tamara L Box

Scott L DuVall

Olga V Patterson

Affiliations

Soon will be listed here.

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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