دورية أكاديمية
Machine Learning-based Characterization of Longitudinal Health Care Utilization Among Patients With Inflammatory Bowel Diseases.
| العنوان: | Machine Learning-based Characterization of Longitudinal Health Care Utilization Among Patients With Inflammatory Bowel Diseases. |
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| المؤلفون: | Limketkai BN; Center for Inflammatory Bowel Diseases, Vatche and Tamar Manoukian Division of Digestive Diseases, UCLA School of Medicine, Los Angeles, CA, USA., Maas L; Division of Gastroenterology and Hepatology, Johns Hopkins University School of Medicine, Baltimore, MD, USA., Krishna M; Division of Gastroenterology and Hepatology, Johns Hopkins University School of Medicine, Baltimore, MD, USA., Dua A; Center for Inflammatory Bowel Diseases, Vatche and Tamar Manoukian Division of Digestive Diseases, UCLA School of Medicine, Los Angeles, CA, USA., DeDecker L; Center for Inflammatory Bowel Diseases, Vatche and Tamar Manoukian Division of Digestive Diseases, UCLA School of Medicine, Los Angeles, CA, USA., Sauk JS; Center for Inflammatory Bowel Diseases, Vatche and Tamar Manoukian Division of Digestive Diseases, UCLA School of Medicine, Los Angeles, CA, USA., Parian AM; Division of Gastroenterology and Hepatology, Johns Hopkins University School of Medicine, Baltimore, MD, USA. |
| المصدر: | Inflammatory bowel diseases [Inflamm Bowel Dis] 2024 May 02; Vol. 30 (5), pp. 697-703. |
| نوع المنشور: | Journal Article; Research Support, N.I.H., Extramural; Research Support, Non-U.S. Gov't; Multicenter Study |
| اللغة: | English |
| بيانات الدورية: | 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 |
| أسماء مطبوعة: | Publication: 2018- : [Oxford] : Oxford University Press Original Publication: New York, NY : Raven Press, c1995- |
| مواضيع طبية MeSH: | Machine Learning* , Inflammatory Bowel Diseases*/therapy , Patient Acceptance of Health Care*/statistics & numerical data, Humans ; Female ; Male ; Retrospective Studies ; Adult ; Middle Aged ; Longitudinal Studies |
| مستخلص: | Background: Inflammatory bowel disease (IBD) is associated with increased health care utilization. Forecasting of high resource utilizers could improve resource allocation. In this study, we aimed to develop machine learning models (1) to cluster patients according to clinical utilization patterns and (2) to predict longitudinal utilization patterns based on readily available baseline clinical characteristics. Methods: We conducted a retrospective study of adults with IBD at 2 academic centers between 2015 and 2021. Outcomes included different clinical encounters, new prescriptions of corticosteroids, and initiation of biologic therapy. Machine learning models were developed to characterize health care utilization. Poisson regression compared frequencies of clinical encounters. Results: A total of 1174 IBD patients were followed for more than 5673 12-month observational windows. The clustering method separated patients according to low, medium, and high resource utilizers. In Poisson regression models, compared with low resource utilizers, moderate and high resource utilizers had significantly higher rates of each encounter type. Comparing moderate and high resource utilizers, the latter had greater utilization of each encounter type, except for telephone encounters and biologic therapy initiation. Machine learning models predicted longitudinal health care utilization with 81% to 85% accuracy (area under the receiver operating characteristic curve 0.84-0.90); these were superior to ordinal regression and random choice methods. Conclusion: Machine learning models were able to cluster individuals according to relative health care resource utilization and to accurately predict longitudinal resource utilization using baseline clinical factors. Integration of such models into the electronic medical records could provide a powerful semiautomated tool to guide patient risk assessment, targeted care coordination, and more efficient resource allocation. (© The Author(s) 2023. Published by Oxford University Press on behalf of Crohn’s & Colitis Foundation. All rights reserved. For commercial re-use, please contact reprints@oup.com for reprints and translation rights for reprints. All other permissions can be obtained through our RightsLink service via the Permissions link on the article page on our site—for further information please contact journals.permissions@oup.com.) |
| فهرسة مساهمة: | Keywords: health care utilization; machine learning; quality improvement |
| تواريخ الأحداث: | Date Created: 20230716 Date Completed: 20240502 Latest Revision: 20240517 |
| رمز التحديث: | 20240517 |
| DOI: | 10.1093/ibd/izad127 |
| PMID: | 37454280 |
| قاعدة البيانات: | MEDLINE |
Full Text Finder | تدمد: | 1536-4844 |
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| DOI: | 10.1093/ibd/izad127 |