{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,29]],"date-time":"2026-09-29T13:32:04Z","timestamp":1790688724002,"version":"4.1.0"},"reference-count":45,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2025,5,9]],"date-time":"2025-05-09T00:00:00Z","timestamp":1746748800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2025,5,9]],"date-time":"2025-05-09T00:00:00Z","timestamp":1746748800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"funder":[{"name":"Research Project of the Zhejiang Provincial Medical and Health Science and Technology Plan","award":["2024KY127"],"award-info":[{"award-number":["2024KY127"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Sci Rep"],"DOI":"10.1038\/s41598-025-01404-5","type":"journal-article","created":{"date-parts":[[2025,5,9]],"date-time":"2025-05-09T14:13:34Z","timestamp":1746800014000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["Construction of disability risk prediction model for the elderly based on machine learning"],"prefix":"10.1038","volume":"15","author":[{"given":"Jing","family":"Chen","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yifei","family":"Ren","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jie","family":"Ding","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qingqing","family":"Hu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiajia","family":"Xu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jun","family":"Luo","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhaowen","family":"Wu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ting","family":"Chu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,5,9]]},"reference":[{"key":"1404_CR1","doi-asserted-by":"publisher","first-page":"1293","DOI":"10.1056\/NEJMp2016814","volume":"383","author":"LP Fried","year":"2020","unstructured":"Fried, L. P. & Rowe, J. W. Health in Aging - Past, present, and future. N. Engl. J. Med. 383, 1293\u20131296. https:\/\/doi.org\/10.1056\/NEJMp2016814 (2020).","journal-title":"N. Engl. J. Med."},{"key":"1404_CR2","doi-asserted-by":"publisher","first-page":"5797","DOI":"10.1073\/pnas.1219686110","volume":"110","author":"A Steptoe","year":"2013","unstructured":"Steptoe, A., Shankar, A., Demakakos, P. & Wardle, J. Social isolation, loneliness, and all-cause mortality in older men and women. Proc. Natl. Acad. Sci. USA. 110, 5797\u20135801. https:\/\/doi.org\/10.1073\/pnas.1219686110 (2013).","journal-title":"Proc. Natl. Acad. Sci. U.S.A."},{"key":"1404_CR3","doi-asserted-by":"publisher","first-page":"864","DOI":"10.1016\/j.jamda.2020.03.032","volume":"21","author":"J Zhou","year":"2020","unstructured":"Zhou, J. et al. Development and validation of a nomogram for predicting the 6-Year risk of cognitive impairment among Chinese older adults. J. Am. Med. Dir. Assoc. 21, 864\u2013871e866. https:\/\/doi.org\/10.1016\/j.jamda.2020.03.032 (2020).","journal-title":"J. Am. Med. Dir. Assoc."},{"key":"1404_CR4","doi-asserted-by":"publisher","first-page":"593","DOI":"10.46234\/ccdcw2021.158","volume":"3","author":"Y Luo","year":"2021","unstructured":"Luo, Y., Su, B. & Zheng, X. Trends and challenges for population and health during population Aging - China, 2015\u20132050. China CDC Wkly. 3, 593\u2013598. https:\/\/doi.org\/10.46234\/ccdcw2021.158 (2021).","journal-title":"China CDC Wkly."},{"key":"1404_CR5","doi-asserted-by":"publisher","first-page":"59","DOI":"10.1186\/s12877-016-0232-3","volume":"16","author":"LA Reichardt","year":"2016","unstructured":"Reichardt, L. A. et al. Unravelling the potential mechanisms behind hospitalization-associated disability in older patients; the Hospital-Associated disability and impact on daily life (Hospital-ADL) cohort study protocol. BMC Geriatr. 16, 59. https:\/\/doi.org\/10.1186\/s12877-016-0232-3 (2016).","journal-title":"BMC Geriatr."},{"key":"1404_CR6","doi-asserted-by":"publisher","unstructured":"da Rosa, P. P. S., Marques, L. P., Corr\u00eaa, V. P., De Oliveira, C. & Schneider, I. J. C. Is the combination of depression symptoms and Multimorbidity associated with the increase of the prevalence of functional disabilities in Brazilian older adults? A cross-sectional study. Front. Aging. 4 https:\/\/doi.org\/10.3389\/fragi.2023.1188552 (2023).","DOI":"10.3389\/fragi.2023.1188552"},{"key":"1404_CR7","doi-asserted-by":"publisher","first-page":"106226","DOI":"10.1016\/j.ypmed.2020.106226","volume":"141","author":"TN Townsend","year":"2020","unstructured":"Townsend, T. N. & Mehta, N. K. Contributions of obesity and cigarette smoking to incident disability: A longitudinal analysis. Prev. Med. 141, 106226. https:\/\/doi.org\/10.1016\/j.ypmed.2020.106226 (2020).","journal-title":"Prev. Med."},{"key":"1404_CR8","doi-asserted-by":"publisher","first-page":"108210","DOI":"10.1016\/j.ypmed.2024.108210","volume":"191","author":"R Yamaguchi","year":"2024","unstructured":"Yamaguchi, R., Makino, K., Katayama, O., Yamagiwa, D. & Shimada, H. Relationship between self-rated health, physical frailty, and incidence of disability among Japanese community-dwelling older adults: A longitudinal prospective cohort study. Prev. Med. 191, 108210. https:\/\/doi.org\/10.1016\/j.ypmed.2024.108210 (2024).","journal-title":"Prev. Med."},{"key":"1404_CR9","doi-asserted-by":"publisher","first-page":"e53548","DOI":"10.2196\/53548","volume":"7","author":"M Luo","year":"2024","unstructured":"Luo, M. et al. Sleep duration and functional disability among Chinese older adults: Cross-sectional study. JMIR Aging. 7, e53548. https:\/\/doi.org\/10.2196\/53548 (2024).","journal-title":"JMIR Aging"},{"key":"1404_CR10","doi-asserted-by":"publisher","first-page":"466","DOI":"10.11236\/jph.23-111","volume":"71","author":"H Matsuzaki","year":"2024","unstructured":"Matsuzaki, H. et al. Cut-off point of the risk assessment scale for the 9-year risk of functional disability]. [Nihon Koshu Eisei Zasshi] Japanese J. Public. Health 71, 466\u2013473. https:\/\/doi.org\/10.11236\/jph.23-111 (2024).","journal-title":"[Nihon Koshu Eisei Zasshi] Japanese J. Public. Health"},{"key":"1404_CR11","doi-asserted-by":"publisher","first-page":"1040","DOI":"10.1111\/psyg.13156","volume":"24","author":"R Wei","year":"2024","unstructured":"Wei, R. Enhancing the predictive models for disability in older adults with hypertension: Recommendations for future research. Psychogeriatrics: Official J. Japanese Psychogeriatr. Soc. 24, 1040\u20131041. https:\/\/doi.org\/10.1111\/psyg.13156 (2024).","journal-title":"Psychogeriatrics: Official J. Japanese Psychogeriatr. Soc."},{"key":"1404_CR12","doi-asserted-by":"publisher","first-page":"1261","DOI":"10.1016\/j.jclinepi.2008.05.001","volume":"61","author":"T Sims","year":"2008","unstructured":"Sims, T. et al. Simple counts of ADL dependencies do not adequately reflect older adults\u2019 preferences toward States of functional impairment. J. Clin. Epidemiol. 61, 1261\u20131270. https:\/\/doi.org\/10.1016\/j.jclinepi.2008.05.001 (2008).","journal-title":"J. Clin. Epidemiol."},{"key":"1404_CR13","doi-asserted-by":"publisher","first-page":"311","DOI":"10.1186\/s12877-021-02223-9","volume":"21","author":"L Zhang","year":"2021","unstructured":"Zhang, L. et al. A web-based dynamic nomogram for predicting instrumental activities of daily living disability in older adults: A nationally representative survey in China. BMC Geriatr. 21, 311. https:\/\/doi.org\/10.1186\/s12877-021-02223-9 (2021).","journal-title":"BMC Geriatr."},{"key":"1404_CR14","doi-asserted-by":"publisher","first-page":"1583","DOI":"10.1007\/s40520-022-02105-z","volume":"34","author":"W Qi","year":"2022","unstructured":"Qi, W., Yin, Z., Sun, Y., Wei, L. & Wu, Y. Nomogram for predicting the 12-year risk of ADL disability among older adults. Aging Clin. Exp. Res. 34, 1583\u20131591. https:\/\/doi.org\/10.1007\/s40520-022-02105-z (2022).","journal-title":"Aging Clin. Exp. Res."},{"key":"1404_CR15","doi-asserted-by":"publisher","first-page":"104835","DOI":"10.1016\/j.archger.2022.104835","volume":"105","author":"C Xiang","year":"2023","unstructured":"Xiang, C., Wu, Y., Jia, M. & Fang, Y. Machine learning-based prediction of disability risk in geriatric patients with hypertension for different time intervals. Arch. Gerontol. Geriatr. 105, 104835. https:\/\/doi.org\/10.1016\/j.archger.2022.104835 (2023).","journal-title":"Arch. Gerontol. Geriatr."},{"key":"1404_CR16","doi-asserted-by":"publisher","first-page":"1072","DOI":"10.1093\/gerona\/glab269","volume":"77","author":"JL Speiser","year":"2022","unstructured":"Speiser, J. L. et al. Predicting future mobility limitation in older adults: A machine learning analysis of health ABC study data. J. Gerontol. A 77, 1072\u20131078. https:\/\/doi.org\/10.1093\/gerona\/glab269 (2022).","journal-title":"J. Gerontol. A"},{"key":"1404_CR17","doi-asserted-by":"publisher","first-page":"105552","DOI":"10.1016\/j.ijmedinf.2024.105552","volume":"191","author":"Y Wu","year":"2024","unstructured":"Wu, Y. et al. Examining individual and contextual predictors of disability in Chinese older adults: A machine learning approach. Int. J. Med. Informatics. 191, 105552. https:\/\/doi.org\/10.1016\/j.ijmedinf.2024.105552 (2024).","journal-title":"Int. J. Med. Informatics"},{"key":"1404_CR18","doi-asserted-by":"publisher","first-page":"2306","DOI":"10.1007\/s10943-020-01151-z","volume":"60","author":"R Nooripour","year":"2021","unstructured":"Nooripour, R. et al. How resiliency and hope can predict stress of Covid-19 by mediating role of spiritual Well-being based on machine learning. J. Relig. Health. 60, 2306\u20132321. https:\/\/doi.org\/10.1007\/s10943-020-01151-z (2021).","journal-title":"J. Relig. Health"},{"key":"1404_CR19","doi-asserted-by":"publisher","unstructured":"Senoner, T. et al. Identifying the location of an accessory pathway in Pre-Excitation syndromes using an artificial Intelligence-Based algorithm. J. Clin. Med. 10 https:\/\/doi.org\/10.3390\/jcm10194394 (2021).","DOI":"10.3390\/jcm10194394"},{"key":"1404_CR20","doi-asserted-by":"publisher","first-page":"334","DOI":"10.1016\/j.jjcc.2021.08.029","volume":"79","author":"S Sawano","year":"2022","unstructured":"Sawano, S. et al. Deep learning model to detect significant aortic regurgitation using electrocardiography. J. Cardiol. 79, 334\u2013341. https:\/\/doi.org\/10.1016\/j.jjcc.2021.08.029 (2022).","journal-title":"J. Cardiol."},{"key":"1404_CR21","doi-asserted-by":"publisher","first-page":"820240","DOI":"10.3389\/fphys.2021.820240","volume":"12","author":"J Liu","year":"2021","unstructured":"Liu, J. et al. Clinical parameters and metabolomic biomarkers that predict inhospital outcomes in patients with ST-Segment elevated myocardial infarctions. Front. Physiol. 12, 820240. https:\/\/doi.org\/10.3389\/fphys.2021.820240 (2021).","journal-title":"Front. Physiol."},{"key":"1404_CR22","doi-asserted-by":"publisher","first-page":"e0231166","DOI":"10.1371\/journal.pone.0231166","volume":"15","author":"E Zihni","year":"2020","unstructured":"Zihni, E. et al. Opening the black box of artificial intelligence for clinical decision support: A study predicting stroke outcome. PLoS One. 15, e0231166. https:\/\/doi.org\/10.1371\/journal.pone.0231166 (2020).","journal-title":"PLoS One"},{"key":"1404_CR23","doi-asserted-by":"publisher","first-page":"105405","DOI":"10.1016\/j.aap.2019.105405","volume":"136","author":"AB Parsa","year":"2020","unstructured":"Parsa, A. B., Movahedi, A., Taghipour, H., Derrible, S. & Mohammadian, A. K. Toward safer highways, application of XGBoost and SHAP for real-time accident detection and feature analysis. Accid. Anal. Prev. 136, 105405. https:\/\/doi.org\/10.1016\/j.aap.2019.105405 (2020).","journal-title":"Accid. Anal. Prev."},{"key":"1404_CR24","doi-asserted-by":"publisher","first-page":"61","DOI":"10.1093\/ije\/dys203","volume":"43","author":"Y Zhao","year":"2014","unstructured":"Zhao, Y., Hu, Y., Smith, J. P., Strauss, J. & Yang, G. Cohort profile: the China health and retirement longitudinal study (CHARLS). Int. J. Epidemiol. 43, 61\u201368. https:\/\/doi.org\/10.1093\/ije\/dys203 (2014).","journal-title":"Int. J. Epidemiol."},{"key":"1404_CR25","doi-asserted-by":"publisher","first-page":"914","DOI":"10.1001\/jama.1963.03060120024016","volume":"185","author":"S Katz","year":"1963","unstructured":"Katz, S. et al. A standardized measure of biological and psychosocial function. Jama 185, 914\u2013919. https:\/\/doi.org\/10.1001\/jama.1963.03060120024016 (1963).","journal-title":"Jama"},{"key":"1404_CR26","doi-asserted-by":"publisher","first-page":"285","DOI":"10.1093\/geronj\/19.3.285","volume":"19","author":"S Katz","year":"1964","unstructured":"Katz, S. et al. J. Gerontol. 19, 285\u2013293, doi:https:\/\/doi.org\/10.1093\/geronj\/19.3.285 (1964).","journal-title":"J. Gerontol."},{"key":"1404_CR27","doi-asserted-by":"publisher","first-page":"1380657","DOI":"10.3389\/fpubh.2024.1380657","volume":"12","author":"S Cheng","year":"2024","unstructured":"Cheng, S. et al. Trajectories and influencing factors of cognitive function and physical disability in Chinese older people. Front. Public. Health 12, 1380657. https:\/\/doi.org\/10.3389\/fpubh.2024.1380657 (2024).","journal-title":"Front. Public. Health"},{"key":"1404_CR28","doi-asserted-by":"publisher","first-page":"190","DOI":"10.1186\/s12877-023-03843-z","volume":"23","author":"Y Wu","year":"2023","unstructured":"Wu, Y., Xiang, C., Jia, M. & Fang, Y. Correction: interpretable classifiers for prediction of disability trajectories using a nationwide longitudinal database. BMC Geriatr. 23, 190. https:\/\/doi.org\/10.1186\/s12877-023-03843-z (2023).","journal-title":"BMC Geriatr."},{"key":"1404_CR29","doi-asserted-by":"publisher","first-page":"2486","DOI":"10.1007\/s11606-023-08215-2","volume":"38","author":"Y Lu","year":"2023","unstructured":"Lu, Y. et al. Machine Learning-Based prediction of functional disability: A cohort study of Japanese older adults in 2013\u20132019. J. Gen. Intern. Med. 38, 2486\u20132493. https:\/\/doi.org\/10.1007\/s11606-023-08215-2 (2023).","journal-title":"J. Gen. Intern. Med."},{"key":"1404_CR30","doi-asserted-by":"publisher","first-page":"1054208","DOI":"10.3389\/fcvm.2022.1054208","volume":"9","author":"S Lu","year":"2022","unstructured":"Lu, S. et al. Prevalence and possible factors of cognitive frailty in the elderly with hypertension and diabetes. Front. Cardiovasc. Med. 9, 1054208. https:\/\/doi.org\/10.3389\/fcvm.2022.1054208 (2022).","journal-title":"Front. Cardiovasc. Med."},{"key":"1404_CR31","doi-asserted-by":"publisher","first-page":"77","DOI":"10.1016\/S0749-3797(18)30622-6","volume":"10","author":"EM Andresen","year":"1994","unstructured":"Andresen, E. M., Malmgren, J. A., Carter, W. B. & Patrick, D. L. Screening for depression in well older adults: Evaluation of a short form of the CES-D (Center for epidemiologic studies depression Scale). Am. J. Prev. Med. 10, 77\u201384 (1994).","journal-title":"Am. J. Prev. Med."},{"key":"1404_CR32","doi-asserted-by":"publisher","first-page":"1657","DOI":"10.14309\/ajg.0000000000000632","volume":"115","author":"F Deshmukh","year":"2020","unstructured":"Deshmukh, F. & Merchant, S. S. Explainable machine learning model for predicting GI bleed mortality in the intensive care unit. Am. J. Gastroenterol. 115, 1657\u20131668. https:\/\/doi.org\/10.14309\/ajg.0000000000000632 (2020).","journal-title":"Am. J. Gastroenterol."},{"key":"1404_CR33","doi-asserted-by":"publisher","first-page":"645513","DOI":"10.3389\/ijph.2021.645513","volume":"66","author":"L Enroth","year":"2021","unstructured":"Enroth, L. & Fors, S. Trends in the social class inequalities in disability and Self-Rated health: Repeated Cross-Sectional surveys from Finland and Sweden 2001\u20132018. Int. J. Public. Health 66, 645513. https:\/\/doi.org\/10.3389\/ijph.2021.645513 (2021).","journal-title":"Int. J. Public. Health"},{"key":"1404_CR34","doi-asserted-by":"publisher","first-page":"e0220157","DOI":"10.1371\/journal.pone.0220157","volume":"14","author":"JM Carmona-Torres","year":"2019","unstructured":"Carmona-Torres, J. M. et al. Disability for basic and instrumental activities of daily living in older individuals. PloS One 14, e0220157. https:\/\/doi.org\/10.1371\/journal.pone.0220157 (2019).","journal-title":"PloS One"},{"key":"1404_CR35","doi-asserted-by":"publisher","first-page":"1009","DOI":"10.1016\/j.jpain.2023.01.014","volume":"24","author":"F Sun","year":"2023","unstructured":"Sun, F., Zimmer, Z. & Zajacova, A. Pain and disability transitions among older Americans: The role of education. J. Pain. 24, 1009\u20131019. https:\/\/doi.org\/10.1016\/j.jpain.2023.01.014 (2023).","journal-title":"J. Pain"},{"key":"1404_CR36","doi-asserted-by":"publisher","first-page":"90","DOI":"10.1016\/j.sleep.2023.06.017","volume":"109","author":"Y Peng","year":"2023","unstructured":"Peng, Y. et al. Association of nocturnal sleep duration and nocturnal sleep changes with instrumental activities of daily living disability among middle-aged and elderly Chinese. Sleep Med. 109, 90\u201397. https:\/\/doi.org\/10.1016\/j.sleep.2023.06.017 (2023).","journal-title":"Sleep Med."},{"key":"1404_CR37","doi-asserted-by":"publisher","first-page":"158","DOI":"10.1097\/psy.0000000000000763","volume":"82","author":"L Dong","year":"2020","unstructured":"Dong, L., Freedman, V. A. & de Mendes, C. F. The association of comorbid depression and anxiety symptoms with disability onset in older adults. Psychosom. Med. 82, 158\u2013164. https:\/\/doi.org\/10.1097\/psy.0000000000000763 (2020).","journal-title":"Psychosom. Med."},{"key":"1404_CR38","doi-asserted-by":"publisher","first-page":"1180","DOI":"10.1186\/s12889-023-16100-0","volume":"23","author":"J Wang","year":"2023","unstructured":"Wang, J. et al. Exploring the reciprocal relationship between activities of daily living disability and depressive symptoms among middle-aged and older Chinese people: A four-wave, cross-lagged model. BMC Public. Health. 23, 1180. https:\/\/doi.org\/10.1186\/s12889-023-16100-0 (2023).","journal-title":"BMC Public. Health"},{"key":"1404_CR39","doi-asserted-by":"publisher","unstructured":"Yu, Q. et al. Predicting functional dependency using machine learning among a middle-aged and older Chinese population. Arch. Gerontol. Geriatr. 115 https:\/\/doi.org\/10.1016\/j.archger.2023.105124 (2023).","DOI":"10.1016\/j.archger.2023.105124"},{"key":"1404_CR40","doi-asserted-by":"publisher","first-page":"1225","DOI":"10.1080\/13607863.2019.1597013","volume":"24","author":"RT Morin","year":"2020","unstructured":"Morin, R. T., Nelson, C., Bickford, D., Insel, P. S. & Mackin, R. S. Somatic and anxiety symptoms of depression are associated with disability in late life depression. Aging Ment. Health 24, 1225\u20131228. https:\/\/doi.org\/10.1080\/13607863.2019.1597013 (2020).","journal-title":"Aging Ment. Health"},{"key":"1404_CR41","doi-asserted-by":"publisher","first-page":"S538","DOI":"10.1111\/jgs.15801","volume":"67","author":"D Kong","year":"2019","unstructured":"Kong, D., Solomon, P. & Dong, X. Depressive symptoms and onset of functional disability over 2 years: A prospective cohort study. J. Am. Geriatr. Soc. 67, S538\u2013s544. https:\/\/doi.org\/10.1111\/jgs.15801 (2019).","journal-title":"J. Am. Geriatr. Soc."},{"key":"1404_CR42","doi-asserted-by":"publisher","first-page":"806","DOI":"10.1186\/s12877-024-05409-z","volume":"24","author":"J Zhou","year":"2024","unstructured":"Zhou, J. et al. Risk prediction models for disability in older adults: A systematic review and critical appraisal. BMC Geriatr. 24, 806. https:\/\/doi.org\/10.1186\/s12877-024-05409-z (2024).","journal-title":"BMC Geriatr."},{"key":"1404_CR43","doi-asserted-by":"publisher","first-page":"105567","DOI":"10.1016\/j.cmpb.2020.105567","volume":"196","author":"L Wang","year":"2020","unstructured":"Wang, L. et al. Classifying 2-year recurrence in patients with Dlbcl using clinical variables with imbalanced data and machine learning methods. Comput. Methods Programs Biomed. 196, 105567. https:\/\/doi.org\/10.1016\/j.cmpb.2020.105567 (2020).","journal-title":"Comput. Methods Programs Biomed."},{"key":"1404_CR44","doi-asserted-by":"publisher","first-page":"1271595","DOI":"10.3389\/fpubh.2023.1271595","volume":"11","author":"Y Han","year":"2023","unstructured":"Han, Y. & Wang, S. Disability risk prediction model based on machine learning among Chinese healthy older adults: Results from the China health and retirement longitudinal study. Front. Public. Health 11, 1271595. https:\/\/doi.org\/10.3389\/fpubh.2023.1271595 (2023).","journal-title":"Front. Public. Health"},{"key":"1404_CR45","doi-asserted-by":"publisher","first-page":"500","DOI":"10.1093\/eurheartj\/ehw188","volume":"38","author":"M Motwani","year":"2017","unstructured":"Motwani, M. et al. Machine learning for prediction of all-cause mortality in patients with suspected coronary artery disease: A 5-year multicentre prospective registry analysis. Eur. Heart J. 38, 500\u2013507. https:\/\/doi.org\/10.1093\/eurheartj\/ehw188 (2017).","journal-title":"Eur. Heart J."}],"container-title":["Scientific Reports"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.nature.com\/articles\/s41598-025-01404-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41598-025-01404-5","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41598-025-01404-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,5,9]],"date-time":"2025-05-09T14:13:35Z","timestamp":1746800015000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.nature.com\/articles\/s41598-025-01404-5"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,5,9]]},"references-count":45,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2025,12]]}},"alternative-id":["1404"],"URL":"https:\/\/doi.org\/10.1038\/s41598-025-01404-5","relation":{},"ISSN":["2045-2322"],"issn-type":[{"value":"2045-2322","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,5,9]]},"assertion":[{"value":"26 January 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"6 May 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"9 May 2025","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare no competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"16247"}}