期刊
INFLAMMATORY BOWEL DISEASES
卷 29, 期 4, 页码 503-510出版社
OXFORD UNIV PRESS INC
DOI: 10.1093/ibd/izac109
关键词
Natural language processing; inflammatory bowel disease; extra-intestinal manifestations; machine learning; artificial intelligence; prediction models
This study developed a natural language processing (NLP) system to identify the presence and status of extraintestinal manifestations (EIMs) in inflammatory bowel disease (IBD) patients using clinical notes. The results showed that NLP methods can accurately detect and infer the activity status of EIMs, offering exciting possibilities for population-based research and decision support in IBD.
Background Extraintestinal manifestations (EIMs) occur commonly in inflammatory bowel disease (IBD), but population-level understanding of EIM behavior is difficult. We present a natural language processing (NLP) system designed to identify both the presence and status of EIMs using clinical notes from patients with IBD. Methods In a single-center retrospective study, clinical outpatient electronic documents were collected in patients with IBD. An NLP EIM detection pipeline was designed to determine general and specific symptomatic EIM activity status descriptions using Python 3.6. Accuracy, sensitivity, and specificity, and agreement using Cohen's kappa coefficient were used to compare NLP-inferred EIM status to human documentation labels. Results The 1240 individuals identified as having at least 1 EIM consisted of 54.4% arthritis, 17.2% ocular, and 17.0% psoriasiform EIMs. Agreement between reviewers on EIM status was very good across all EIMs (kappa = 0.74; 95% confidence interval [CI], 0.70-0.78). The automated NLP pipeline determining general EIM activity status had an accuracy, sensitivity, specificity, and agreement of 94.1%, 0.92, 0.95, and kappa = 0.76 (95% CI, 0.74-0.79), respectively. Comparatively, prediction of EIM status using administrative codes had a poor sensitivity, specificity, and agreement with human reviewers of 0.32, 0.83, and kappa = 0.26 (95% CI, 0.20-0.32), respectively. Conclusions NLP methods can both detect and infer the activity status of EIMs using the medical document an information source. Though source document variation and ambiguity present challenges, NLP offers exciting possibilities for population-based research and decision support in IBD.
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