This paper is in the following e-collection/theme issue:

Natural Language Processing (692) Digital Pain Assessment and Management (164) Electronic Health Records (1053) Ontologies, Classifications, and Coding (239)

Published on in Vol 8, No 11 (2020): November

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/18659, first published .
Characterizing Chronic Pain Episodes in Clinical Text at Two Health Care Systems: Comprehensive Annotation and Corpus Analysis

Characterizing Chronic Pain Episodes in Clinical Text at Two Health Care Systems: Comprehensive Annotation and Corpus Analysis

Characterizing Chronic Pain Episodes in Clinical Text at Two Health Care Systems: Comprehensive Annotation and Corpus Analysis

Authors of this article:

Luke A Carlson1 Author Orcid Image ;   Molly M Jeffery2 Author Orcid Image ;   Sunyang Fu1 Author Orcid Image ;   Huan He1 Author Orcid Image ;   Rozalina G McCoy3 Author Orcid Image ;   Yanshan Wang1 Author Orcid Image ;   William Michael Hooten4 Author Orcid Image ;   Jennifer St Sauver5 Author Orcid Image ;   Hongfang Liu1 Author Orcid Image ;   Jungwei Fan1 Author Orcid Image
Article Authors Cited by (2) Tweetations (2) Metrics

Journals

  1. Chaturvedi J, Chance N, Mirza L, Vernugopan V, Velupillai S, Stewart R, Roberts A. Development of a Corpus Annotated With Mentions of Pain in Mental Health Records: Natural Language Processing Approach. JMIR Formative Research 2023;7:e45849 View
  2. Dang Y, Li F, Hu X, Keloth V, Zhang M, Fu S, Amith M, Fan J, Du J, Yu E, Liu H, Jiang X, Xu H, Tao C. Systematic design and data-driven evaluation of social determinants of health ontology (SDoHO). Journal of the American Medical Informatics Association 2023;30(9):1465 View