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Identifying Axial Spondyloarthritis in Electronic Medical Records of US Veterans
Author(s) -
Walsh Jessica A.,
Shao Yijun,
Leng Jianwei,
He Tao,
Teng ChiaChen,
Redd Doug,
Treitler Zeng Qing,
Burningham Zachary,
Clegg Daniel O.,
Sauer Brian C.
Publication year - 2017
Publication title -
arthritis care and research
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.032
H-Index - 163
eISSN - 2151-4658
pISSN - 2151-464X
DOI - 10.1002/acr.23140
Subject(s) - sacroiliitis , medicine , artificial intelligence , information retrieval , natural language processing , machine learning , annotation , medical physics , ankylosing spondylitis , computer science , surgery
Objective Large database research in axial spondyloarthritis (SpA) is limited by a lack of methods for identifying most types of axial SpA. Our objective was to develop methods for identifying axial SpA concepts in the free text of documents from electronic medical records. Methods Veterans with documents in the national Veterans Health Administration Corporate Data Warehouse between January 1, 2005 and June 30, 2015 were included. Methods were developed for exploring, selecting, and extracting meaningful terms that were likely to represent axial SpA concepts. With annotation, clinical experts reviewed sections of text containing the meaningful terms (snippets) and classified the snippets according to whether or not they represented the intended axial SpA concept. With natural language processing (NLP) tools, computers were trained to replicate the clinical experts' snippet classifications. Results Three axial SpA concepts were selected by clinical experts, including sacroiliitis, terms including the prefix spond*, and HLA–B27 positivity (HLA–B27+). With supervised machine learning on annotated snippets, NLP models were developed with accuracies of 91.1% for sacroiliitis, 93.5% for spond*, and 97.2% for HLA–B27+. With independent validation, the accuracies were 92.0% for sacroiliitis, 91.0% for spond*, and 99.0% for HLA–B27+. Conclusion We developed feasible and accurate methods for identifying axial SpA concepts in the free text of clinical notes. Additional research is required to determine combinations of concepts that will accurately identify axial SpA phenotypes. These novel methods will facilitate previously impractical observational research in axial SpA and may be applied to research with other diseases.