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.
@article{arxiv.2106.07799,
title = {Launching into clinical space with medspaCy: a new clinical text processing toolkit in Python},
author = {Hannah Eyre and Alec B Chapman and Kelly S Peterson and Jianlin Shi and Patrick R Alba and Makoto M Jones and Tamara L Box and Scott L DuVall and Olga V Patterson},
journal= {arXiv preprint arXiv:2106.07799},
year = {2021}
}