{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,10]],"date-time":"2026-03-10T21:04:07Z","timestamp":1773176647199,"version":"3.50.1"},"reference-count":39,"publisher":"Wiley","issue":"3","license":[{"start":{"date-parts":[[2024,3,21]],"date-time":"2024-03-21T00:00:00Z","timestamp":1710979200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["81973144"],"award-info":[{"award-number":["81973144"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2022YFC3603000"],"award-info":[{"award-number":["2022YFC3603000"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Psychogeriatrics"],"published-print":{"date-parts":[[2024,5]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:sec><jats:title>Background<\/jats:title><jats:p>Older adults with hypertension have a high risk of disability, while an accurate risk prediction model is still lacking. This study aimed to construct interpretable disability prediction models for older Chinese with hypertension based on multiple time intervals.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>Data were collected from the Chinese Longitudinal Healthy Longevity and Happy Family Study for 2008\u20132018. A total of 1602, 1108, and 537 older adults were included for the periods of 2008\u20132012, 2008\u20132014, and 2008\u20132018, respectively. Disability was measured by basic activities of daily living. Least absolute shrinkage and selection operator (LASSO) was applied for feature selection. Five machine learning algorithms combined with LASSO set and full\u2010variable set were used to predict 4\u2010, 6\u2010, and 10\u2010year disability risk, respectively. Area under the receiver operating characteristic curve was used as the main metric for selection of the optimal model. SHapley Additive exPlanations (SHAP) was used to explore important predictors of the optimal model.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>Random forest in full\u2010variable set and XGBoost in LASSO set were the optimal models for 4\u2010year prediction. Support vector machine was the optimal model for 6\u2010year prediction on both sets. For 10\u2010year prediction, deep neural network in full variable set and logistic regression in LASSO set were optimal models. Age ranked the most important predictor. Marital status, body mass index, score of Mini\u2010Mental State Examination, and psychological well\u2010being score were also important predictors.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusions<\/jats:title><jats:p>Machine learning shows promise in screening out older adults at high risk of disability. Disability prevention strategies should specifically focus on older patients with unfortunate marriage, high BMI, and poor cognitive and psychological conditions.<\/jats:p><\/jats:sec>","DOI":"10.1111\/psyg.13112","type":"journal-article","created":{"date-parts":[[2024,3,22]],"date-time":"2024-03-22T03:10:26Z","timestamp":1711077026000},"page":"645-654","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Interpretable prediction models for disability in older adults with hypertension: the Chinese Longitudinal Healthy Longevity and Happy Family Study"],"prefix":"10.1111","volume":"24","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3937-0263","authenticated-orcid":false,"given":"Yafei","family":"Wu","sequence":"first","affiliation":[{"name":"School of Public Health Xiamen University  Xiamen China"},{"name":"Key Laboratory of Health Technology Assessment of Fujian Province  Xiamen China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chaoyi","family":"Xiang","sequence":"additional","affiliation":[{"name":"School of Public Health Xiamen University  Xiamen China"},{"name":"Key Laboratory of Health Technology Assessment of Fujian Province  Xiamen China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zongjie","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Public Health Xiamen University  Xiamen China"},{"name":"Key Laboratory of Health Technology Assessment of Fujian Province  Xiamen China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ya","family":"Fang","sequence":"additional","affiliation":[{"name":"School of Public Health Xiamen University  Xiamen China"},{"name":"Key Laboratory of Health Technology Assessment of Fujian Province  Xiamen China"},{"name":"National Institute for Data Science in Health and Medicine Xiamen University  Xiamen China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","published-online":{"date-parts":[[2024,3,21]]},"reference":[{"key":"e_1_2_10_2_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jacc.2017.04.052"},{"key":"e_1_2_10_3_1","first-page":"146","article-title":"Managing hypertension in the elderly: a common chronic disease with increasing age","volume":"5","author":"Nguyen QT","year":"2012","journal-title":"Am Health Drug Benefits"},{"key":"e_1_2_10_4_1","first-page":"67","article-title":"2019 chinese guideline for the management of hypertension in the elderly","volume":"16","author":"Hua Q","year":"2019","journal-title":"J Geriatr Cardiol"},{"key":"e_1_2_10_5_1","doi-asserted-by":"publisher","DOI":"10.1186\/s12913-016-1536-x"},{"key":"e_1_2_10_6_1","doi-asserted-by":"publisher","DOI":"10.1097\/MD.0000000000019769"},{"key":"e_1_2_10_7_1","doi-asserted-by":"publisher","DOI":"10.1097\/CIN.0000000000000282"},{"key":"e_1_2_10_8_1","doi-asserted-by":"publisher","DOI":"10.1161\/JAHA.119.013924"},{"key":"e_1_2_10_9_1","doi-asserted-by":"publisher","DOI":"10.1161\/CIRCULATIONAHA.115.001593"},{"key":"e_1_2_10_10_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jcin.2019.03.026"},{"key":"e_1_2_10_11_1","first-page":"2222","article-title":"What is machine learning? 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