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
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Heo, Suncheol | - |
dc.contributor.author | Yu, Jae Yong | - |
dc.contributor.author | Kang, Eun Ae | - |
dc.contributor.author | Shin, Hyunah | - |
dc.contributor.author | Ryu, Kyeongmin | - |
dc.contributor.author | Kim, Chungsoo | - |
dc.contributor.author | Chegal, Yebin | - |
dc.contributor.author | Jung, Hyojung | - |
dc.contributor.author | Lee, Suehyun | - |
dc.contributor.author | Park, Rae Woong | - |
dc.contributor.author | Kim, Kwangsoo | - |
dc.contributor.author | Hwangbo, Yul | - |
dc.contributor.author | Lee, Jae-Hyun | - |
dc.contributor.author | Park, Yu Rang | - |
dc.date.accessioned | 2023-12-26T02:30:16Z | - |
dc.date.available | 2023-12-26T02:30:16Z | - |
dc.date.issued | 2023-07 | - |
dc.identifier.issn | 2093-3681 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/gachon/handle/2020.sw.gachon/89819 | - |
dc.description.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 lev-eraged 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 time-series 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 re-ceiver 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 hemato-crit, albumin, prothrombin time, and lymphocytes in predicting DILI. Conclusions: Implementing a multicenter-based time-series 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. | - |
dc.format.extent | 10 | - |
dc.language | 영어 | - |
dc.language.iso | ENG | - |
dc.publisher | Korean Society of Medical Informatics | - |
dc.title | 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 | - |
dc.type | Article | - |
dc.identifier.doi | 10.4258/hir.2023.29.3.246 | - |
dc.identifier.bibliographicCitation | Healthcare Informatics Research, v.29, no.3, pp 246 - 255 | - |
dc.identifier.kciid | ART002985245 | - |
dc.description.isOpenAccess | N | - |
dc.identifier.scopusid | 2-s2.0-85167621544 | - |
dc.citation.endPage | 255 | - |
dc.citation.startPage | 246 | - |
dc.citation.title | Healthcare Informatics Research | - |
dc.citation.volume | 29 | - |
dc.citation.number | 3 | - |
dc.publisher.location | 대한민국 | - |
dc.description.journalRegisteredClass | scopus | - |
dc.description.journalRegisteredClass | esci | - |
dc.description.journalRegisteredClass | kci | - |
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.
1342, Seongnam-daero, Sujeong-gu, Seongnam-si, Gyeonggi-do, Republic of Korea(13120)031-750-5114
COPYRIGHT 2020 Gachon University All Rights Reserved.
Certain data included herein are derived from the © Web of Science of Clarivate Analytics. All rights reserved.
You may not copy or re-distribute this material in whole or in part without the prior written consent of Clarivate Analytics.