Clinical Tractor: A Framework for Automatic Natural Language Understanding of Clinical Practice Guidelines

Daniel R. Schlegel, Kate Gordon, Carmelo Gaudioso, Mor Peleg

Research output: Contribution to journalArticlepeer-review

Abstract

Computational representations of the semantic knowledge embedded within clinical practice guidelines (CPGs) may be a significant aid in creating computer interpretable guidelines (CIGs). Formalizing plain text CPGs into CIGs manually is a laborious and burdensome task, even using CIG tools and languages designed to improve the process. Natural language understanding (NLU) systems perform automated reading comprehension, parsing text and using reasoning to convert syntactic information from unstructured text into semantic information. Influenced by successful systems used in other domains, we present the architecture for a system which uses NLU approaches to create semantic representations of entire CPGs. In the future, these representations may be used to generate CIGs.

Original languageEnglish
Pages (from-to)784-793
Number of pages10
JournalAMIA ... Annual Symposium proceedings / AMIA Symposium. AMIA Symposium
Volume2019
StatePublished - 2019

Bibliographical note

Publisher Copyright:
©2019 AMIA - All rights reserved.

ASJC Scopus subject areas

  • Medicine (all)

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