Automated Machine Learning for Predicting Pressure Injury Risk in Home Care Centers Throughout Taiwan.
Objective: Pressure injuries (PIs) are a common issue among patients, particularly in home care centers. PIs lead to increased complications and increased healthcare costs. This study aimed to predict the occurrence of PIs among patients in Taiwan's home care centers using electronic health records...
| Published in: | Health & Social Care in the Community Vol. 2026; pp. 1 - 19 |
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| Main Authors: | , , , , , |
| Format: | pictorial research tables/charts Journal Article |
| Published: |
Wiley-Blackwell
1/17/2026
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| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=190937135&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 190937135 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 09660410 EVX jtl: Health & Social Care in the Community issn: 09660410 maglogo: Y pubinfo: dt: 1/17/2026 vid: 2026 pid: 480 pub: Wiley-Blackwell place: Malden, Massachusetts artinfo: ui: 190937135 190937135 190937135 10.1155/hsc/5538914 190937135 ppf: 1 ppct: 18 formats: fmt: – @attributes: type: T – @attributes: type: C – @attributes: type: P tig: atl: Automated Machine Learning for Predicting Pressure Injury Risk in Home Care Centers Throughout Taiwan. aug: au: Tsay, Shwu-Feng Lai, Jih Mei Chou, Yen-Ting Liao, Gen-Yih Chen, Ssu-Han Niroumand Sarvandani, Mohammad affil: Department of Nursing and Healthcare,, Ministry of Health and Welfare,, Taipei, Taiwan, mohw.gov.tw sug: subj: Home Health Care Taiwan Pressure Ulcer Risk Factors Risk Assessment Methods Machine Learning Prediction Models Automation Electronic Health Records Utilization Pressure Ulcer Epidemiology Human Taiwan Prediction Algorithms Pressure Ulcer Etiology Retrospective Design Record Review Holistic Care Evaluation Nursing Assessment Prevalence Factorial Design Validity Chronic Disease Polypharmacy Nutritional Status Community Living Pressure Ulcer Prevention and Control Funding Source Scales Braden Scale for Predicting Pressure Sore Risk Barthel Index Geriatric Depression Scale Short Portable Mental Status Questionnaire Questionnaires Clinical Assessment Tools Descriptive Statistics Data Analysis Software ab: Objective: Pressure injuries (PIs) are a common issue among patients, particularly in home care centers. PIs lead to increased complications and increased healthcare costs. This study aimed to predict the occurrence of PIs among patients in Taiwan's home care centers using electronic health records (EHRs) from the proposed Home Care Management System (HCMS). Design: A retrospective study was conducted. Setting and Participants: Nurses completed the holistic healthcare assessment (HHCA) for 36,896 patients. The data collection duration covered the period from October 2021 to May 2023. The study analyzed 44,188 cases, with a PI prevalence of approximately 31%. Methods: This study employed an automated machine learning (AutoML) approach. The AutoML systematically and efficiently selected modeling strategies and tuned hyperparameters using a partial factorial design. This approach reduced the number of trials needed for optimal model performance. Results: On the independent hold‐out testing set, the final model achieved an accuracy of 77.56% and an AUC of 81.82%. Not only can top risk factors be identified but also the Shapley additive explanations (SHAP) are employed at an individual level over time. Conclusions and Implications: This study represents one of the first large‐scale applications of AutoML for PI prediction in the home care setting, a context that differs substantially from both nursing homes and hospitals. Our findings emphasize Braden assessment, chronic disease burden, polypharmacy, and nutritional status as the dominant predictors in community‐dwelling patients receiving professional home care. By extending predictive analytics into this underexplored domain, the study demonstrates how risk models can be operationalized to support home care nurses. In particular, SHAP‐based waterfall plots guide nurses to detect whether specific risk features are improving or worsening and to adjust care plans accordingly. pubtype: Academic Journal doctype: pictorial research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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