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

Autor:	Eyre, Hannah, Chapman, Alec B, Peterson, Kelly S, Shi, Jianlin, Alba, Patrick R, Jones, Makoto M, Box, Tamara L, DuVall, Scott L, Patterson, Olga V
Rok vydání:	2021
Předmět:	Computer Science - Computation and Language
Druh dokumentu:	Working Paper
Popis:	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. Comment: Accepted to AMIA Annual Symposium 2021
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2106.07799 Zobrazit plný text záznamu View this record from Arxiv