• Identifying the Presence, Acti...

Treffer: Identifying the Presence, Activity, and Status of Extraintestinal Manifestations of Inflammatory Bowel Disease Using Natural Language Processing of Clinical Notes.

Title:
Identifying the Presence, Activity, and Status of Extraintestinal Manifestations of Inflammatory Bowel Disease Using Natural Language Processing of Clinical Notes.
Authors:
Stidham RW; Division of Gastroenterology, Department of Internal Medicine, University of Michigan Medical School, Ann Arbor, MI, USA.; Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA., Yu D; School of Information, University of Michigan, Ann Arbor, MI, USA., Zhao X; Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA., Bishu S; Division of Gastroenterology, Department of Internal Medicine, University of Michigan Medical School, Ann Arbor, MI, USA., Rice M; Division of Gastroenterology, Department of Internal Medicine, University of Michigan Medical School, Ann Arbor, MI, USA., Bourque C; Division of Gastroenterology, Department of Internal Medicine, University of Michigan Medical School, Ann Arbor, MI, USA., Vydiswaran VVG; School of Information, University of Michigan, Ann Arbor, MI, USA.; Department of Learning Health Sciences, University of Michigan Medical School, Ann Arbor, MI, USA.
Source:
Inflammatory bowel diseases [Inflamm Bowel Dis] 2023 Apr 03; Vol. 29 (4), pp. 503-510.
Publication Type:
Journal Article; Research Support, Non-U.S. Gov't
Language:
English
Journal Info:
Publisher: Oxford University Press Country of Publication: England NLM ID: 9508162 Publication Model: Print Cited Medium: Internet ISSN: 1536-4844 (Electronic) Linking ISSN: 10780998 NLM ISO Abbreviation: Inflamm Bowel Dis Subsets: MEDLINE
Imprint Name(s):
Publication: 2018- : [Oxford] : Oxford University Press
Original Publication: New York, NY : Raven Press, c1995-
MeSH Terms:
Crohn Disease*/diagnosis , Inflammatory Bowel Diseases*/diagnosis , Arthritis*, Humans ; Retrospective Studies ; Natural Language Processing
Contributed Indexing:
Keywords: Natural language processing; artificial intelligence; extra-intestinal manifestations; inflammatory bowel disease; machine learning; prediction models
Local Abstract: [plain-language-summary] Extraintestinal manifestations of inflammatory bowel disease impact on patient experience, but are poorly captured by electronic health records. Natural language processing systems are capable of not only detecting extraintestinal manifestations, but also inferring activity information by automated analysis of clinical notes.
Entry Date(s):
Date Created: 20220603 Date Completed: 20230405 Latest Revision: 20230505
Update Code:
20250114
DOI:
10.1093/ibd/izac109
PMID:
35657296
Database:
MEDLINE

Weitere Informationen

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 (κ = 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 κ = 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 κ = 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.
(© The Author(s) 2022. Published by Oxford University Press on behalf of Crohn’s & Colitis Foundation. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)