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Heo S, Yu JY, Kang EA, Shin H, Ryu K, Kim C, Chegal Y, Jung H, Lee S, Park RW, Kim K, Hwangbo Y, Lee JH, Park YR. Development and Verification of Time-Series Deep Learning for Drug-Induced Liver Injury Detection in Patients Taking Angiotensin II Receptor Blockers: A Multicenter Distributed Research Network Approach. Healthc Inform Res 2023; 29:246-255. [PMID: 37591680 PMCID: PMC10440200 DOI: 10.4258/hir.2023.29.3.246] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2023] [Revised: 07/20/2023] [Accepted: 07/23/2023] [Indexed: 08/19/2023] Open
Abstract
OBJECTIVES The objective of this study was to develop and validate a multicenter-based, multi-model, time-series deep learning model for predicting drug-induced liver injury (DILI) in patients taking angiotensin receptor blockers (ARBs). The study leveraged a national-level multicenter approach, utilizing electronic health records (EHRs) from six hospitals in Korea. METHODS A retrospective cohort analysis was conducted using EHRs from six hospitals in Korea, comprising a total of 10,852 patients whose data were converted to the Common Data Model. The study assessed the incidence rate of DILI among patients taking ARBs and compared it to a control group. Temporal patterns of important variables were analyzed using an interpretable timeseries model. RESULTS The overall incidence rate of DILI among patients taking ARBs was found to be 1.09%. The incidence rates varied for each specific ARB drug and institution, with valsartan having the highest rate (1.24%) and olmesartan having the lowest rate (0.83%). The DILI prediction models showed varying performance, measured by the average area under the receiver operating characteristic curve, with telmisartan (0.93), losartan (0.92), and irbesartan (0.90) exhibiting higher classification performance. The aggregated attention scores from the models highlighted the importance of variables such as hematocrit, albumin, prothrombin time, and lymphocytes in predicting DILI. CONCLUSIONS Implementing a multicenter-based timeseries classification model provided evidence that could be valuable to clinicians regarding temporal patterns associated with DILI in ARB users. This information supports informed decisions regarding appropriate drug use and treatment strategies.
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Affiliation(s)
- Suncheol Heo
- Department of Biomedical Systems Informatics, Yonsei University College of Medicine, Seoul,
Korea
| | - Jae Yong Yu
- Department of Biomedical Systems Informatics, Yonsei University College of Medicine, Seoul,
Korea
| | - Eun Ae Kang
- Medical Informatics Collaborative Unit, Department of Research Affairs, Yonsei University College of Medicine, Seoul,
Korea
| | - Hyunah Shin
- Healthcare Data Science Center, Konyang University Hospital, Daejeon,
Korea
| | - Kyeongmin Ryu
- Healthcare Data Science Center, Konyang University Hospital, Daejeon,
Korea
| | - Chungsoo Kim
- Department of Biomedical Sciences, Ajou University Graduate School of Medicine, Seoul,
Korea
| | - Yebin Chegal
- Department of Statistics, Korea University, Suwon,
Korea
| | - Hyojung Jung
- Healthcare AI Team, National Cancer Center, Goyang,
Korea
| | - Suehyun Lee
- Healthcare Data Science Center, Konyang University Hospital, Daejeon,
Korea
| | - Rae Woong Park
- Department of Biomedical Sciences, Ajou University Graduate School of Medicine, Seoul,
Korea
| | - Kwangsoo Kim
- Transdisciplinary Department of Medicine & Advanced Technology, Seoul National University Hospital, Seoul,
Korea
| | - Yul Hwangbo
- Healthcare AI Team, National Cancer Center, Goyang,
Korea
| | - Jae-Hyun Lee
- Division of Allergy and Immunology, Department of Internal Medicine, Institute of Allergy, Yonsei University College of Medicine, Seoul,
Korea
| | - Yu Rang Park
- Department of Biomedical Systems Informatics, Yonsei University College of Medicine, Seoul,
Korea
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