A model for predicting physical function upon discharge of hospitalized older adults in Taiwan - a machine learning approach based on both electronic health records and comprehensive geriatric assessment

Aug 5, 2026

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Predicting physical function at hospital discharge for older adults is critical for planning rehabilitation and improving outcomes. This original research develops a machine-learning prediction model using both electronic health records and comprehensive geriatric assessments for hospitalized older adults in Taiwan. Data from admissions to an acute geriatric unit (2012–2018) were used to train models with 52 clinical features. Compared with logistic regression, XGBoost and random forest showed substantially higher predictive performance, with key drivers including baseline and in-hospital ADL and MNA during admission.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages9

Reading Time23 min

AccessFree to download

Background

Methods

Results

Conclusion

Key findings and features

TYPE Original Research PUBLISHED 21 July 2023 DOI 10.3389/fmed.2023.1160013 OPEN ACCESS EDITED BY Zhe He, Florida State University, United States REVIEWED BY David Hollar, Mercer University School of Medicine, United States Susana López-Ortiz, Miguel de Cervantes European University, Spain *CORRESPONDENCE Shih-Yi Lin [sylin@vghtc.gov.tw](sylin@vghtc.gov.tw)[ ](sylin@vghtc.gov.tw)Chao-Tung Yang [ctyang@thu.edu.tw](ctyang@thu.edu.tw) †These authors have contributed equally to this work RECEIVED 06 February 2023 ACCEPTED 03 July 2023 PUBLISHED 21 July 2023 CITATION Chu W-M, Tsan Y-T, Chen P-Y, Chen C-Y, Hao M-L, Chan W-C, Chen H-M, Hsu P-S, Lin S-Y and Yang C-T (2023) A model for predicting physical function upon discharge of hospitalized older adults in Taiwan—a machine learning approach based on both electronic health records and comprehensive geriatric assessment. Front. Med. 10:1160013 . doi: 10.3389/fmed.2023.1160013 COPYRIGHT © 2023 Chu, Tsan, Chen, Chen, Hao, Chan, Chen, Hsu, Lin and Yang. This is an openaccess article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. A model for predicting physical function upon discharge of hospitalized older adults in Taiwan—a machine learning approach based on both electronic health records and comprehensive geriatric assessment Wei-Min Chu 1, 2, 3, 4, 5, Yu-Tse Tsan 5, 6, Pei-Yu Chen 1, Chia-Yu Chen 1, Man-Ling Hao7, Wei-Chan Chan 6, Hong-Ming Chen 8, Pi-Shan Hsu 1, Shih-Yi Lin 5, 9*† and Chao-Tung Yang7, 10*† 1 Department of Family Medicine, Taichung Veterans General Hospital, Taichung, Taiwan, 2 Education and Innovation Center for Geriatrics and Gerontology, National Center for Geriatrics and Gerontology, Ōbu, Japan, 3School of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan, 4 Department of Post-Baccalaureate Medicine, College of Medicine, National Chung Hsing University, Taichung, Taiwan, 5Geriatrics and Gerontology Research Center, College of Medicine, National Chung Hsing University, Taichung, Taiwan, 6 Department of Occupational Medicine, Taichung Veterans General Hospital, Taichung, Taiwan, 7 Department of Computer Science, Tunghai University, Taichung, Taiwan, 8 Department of Applied Mathematics, Tunghai University, Taichung, Taiwan, 9Center for Geriatrics and Gerontology, Taichung Veterans General Hospital, Taichung, Taiwan, 10 Research Center for Smart Sustainable Circular Economy, Tunghai University, Taichung, Taiwan Background: Predicting physical function upon discharge among hospitalized older adults is important. This study has aimed to develop a prediction model of physical function upon discharge through use of a machine learning algorithm using electronic health records (EHRs) and comprehensive geriatrics assessments (CGAs) among hospitalized older adults in Taiwan. Methods: Data was retrieved from the clinical database of a tertiary medical center in central Taiwan. Older adults admitted to the acute geriatric unit during the period from January 2012 to December 2018 were included for analysis, while those with missing data were excluded. From data of the EHRs and CGAs, a total of 52 clinical features were input for model building. We used 3 different machine learning algorithms, XGBoost, random forest and logistic regression. Results: In total, 1,755 older adults were included in final analysis, with a mean age of 80.68 years. For linear models on physical function upon discharge, the accuracy of prediction was 87% for XGBoost, 85% for random forest, and 32% for logistic regression. For classification models on physica

FAQs

What is the document’s main goal?

To develop a machine-learning prediction model for physical function upon discharge among hospitalized older adults in Taiwan, using electronic health records and comprehensive geriatric assessments.

Which data sources and variables were used to build the model?

Clinical data were retrieved from a tertiary medical center’s database, combining EHRs and CGAs. A total of 52 clinical features were used for model building.

How did different machine learning models perform?

For classification, random forest, logistic regression, and XGBoost achieved 94%, 92%, and 92% accuracy, respectively. auROC reached 98% for XGBoost and random forest, while logistic regression showed 97%.

Aug 5, 2026

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