{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,30]],"date-time":"2026-09-30T04:30:33Z","timestamp":1790742633213,"version":"4.1.0"},"reference-count":40,"publisher":"Wiley","issue":"5","license":[{"start":{"date-parts":[[2025,8,26]],"date-time":"2025-08-26T00:00:00Z","timestamp":1756166400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Health Care Science"],"published-print":{"date-parts":[[2025,10]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Stroke is the second leading cause of death and third leading cause of disability worldwide and is the leading cause of death and disability among adults in China, with its incidence rate continuing to rise. In China, the average age of first\u2010time stroke patients is 66.4 years, and the intravenous thrombolysis rate using recombinant tissue plasminogen activator within 3\u2009h of onset is only 16%. Given this fact, there is a pressing need for real\u2010time predictive tools, particularly for elderly individuals at home, that can provide early warnings for potential strokes.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>We collected continuous monitoring data from nonintrusive smart beds and multimodal temporal data from electronic medical records at the National Center for Neurological Disorders. The data included smart bed monitoring indicators, laboratory tests, nurse observations, and static data as potential predictors, with stroke as the outcome. We applied feature representation and feature selection techniques and then input the predictors into machine learning models. Additionally, deep learning models were used after preprocessing the irregular temporal data. Finally, we evaluated the performance of the stroke prediction models and assessed the importance of the features. We used continuously updated vital signs and clinical data during hospitalization to generate timely stroke risk alerts during the same period of admission.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>A total of 37,041 samples were analyzed, of which 7020 patients were diagnosed with stroke. When only the smart bed features were used for prediction, the model achieved an area under the receiver operating characteristic curve (AUROC) of 0.59\u22120.63, with an accuracy ranging from 60%\u221265%. Among the four artificial intelligence algorithms, the random forest model demonstrated the best performance. After all the available features were incorporated, the AUROC increased to 0.94, and the accuracy improved to 92%.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>In this study, the occurrence of stroke was successfully identified by integrating multimodal temporal data from electronic medical records. Noncontact monitoring of respiration and heart rate offers a promising approach for daily stroke surveillance in home\u2010based populations, particularly for elderly individuals living alone.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/hcs2.70034","type":"journal-article","created":{"date-parts":[[2025,8,26]],"date-time":"2025-08-26T15:40:58Z","timestamp":1756222858000},"page":"340-349","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Noncontact Monitoring and AI\u2010Driven Stroke Prediction: National Center for Neurological Disorders\u2010Based Approach Using Smart Beds"],"prefix":"10.1002","volume":"4","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2161-4936","authenticated-orcid":false,"given":"Lan","family":"Lan","sequence":"first","affiliation":[{"name":"Information Management and Data Center, Beijing Tiantan Hospital Capital Medical University Beijing China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jia\u2010Wei","family":"Luo","sequence":"additional","affiliation":[{"name":"West China Biomedical Big Data Center, West China Hospital Sichuan University Chengdu China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rui","family":"Li","sequence":"additional","affiliation":[{"name":"Information Management and Data Center, Beijing Tiantan Hospital Capital Medical University Beijing China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ling","family":"Guan","sequence":"additional","affiliation":[{"name":"National Center for Neurological Disorders, Beijing Tiantan Hospital Capital Medical University Beijing China"},{"name":"Department of Medicine The University of British Columbia Vancouver British Columbia Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xin","family":"Wang","sequence":"additional","affiliation":[{"name":"Information Management and Data Center, Beijing Tiantan Hospital Capital Medical University Beijing China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jin","family":"Yin","sequence":"additional","affiliation":[{"name":"West China Biomedical Big Data Center, West China Hospital Sichuan University Chengdu China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yi\u2010Long","family":"Wang","sequence":"additional","affiliation":[{"name":"National Center for Neurological Disorders, Beijing Tiantan Hospital Capital Medical University Beijing China"},{"name":"Department of Neurology, Beijing Tiantan Hospital Capital Medical University Beijing China"},{"name":"Chinese Institute for Brain Research Beijing China"},{"name":"Advanced Innovation Center for Human Brain Protection Capital Medical University Beijing China"},{"name":"China National Clinical Research Center for Neurological Diseases Beijing China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2025,8,26]]},"reference":[{"key":"e_1_2_13_2_1","doi-asserted-by":"publisher","DOI":"10.1016\/S1474-4422(21)00252-0"},{"key":"e_1_2_13_3_1","doi-asserted-by":"publisher","DOI":"10.1016\/S0140-6736(19)30427-1"},{"key":"e_1_2_13_4_1","doi-asserted-by":"publisher","DOI":"10.3969\/j.issn.1673-5765.2020.10.001"},{"key":"e_1_2_13_5_1","doi-asserted-by":"publisher","DOI":"10.1161\/circulationaha.116.025250"},{"key":"e_1_2_13_6_1","doi-asserted-by":"publisher","DOI":"10.13558\/j.cnki.issn1672-3686.2017.06.002"},{"key":"e_1_2_13_7_1","doi-asserted-by":"publisher","DOI":"10.1186\/s12883-020-01724-1"},{"key":"e_1_2_13_8_1","doi-asserted-by":"publisher","DOI":"10.3969\/j.issn.1673-5765.2014.09.005"},{"key":"e_1_2_13_9_1","doi-asserted-by":"publisher","DOI":"10.1136\/bmj.m441"},{"key":"e_1_2_13_10_1","doi-asserted-by":"publisher","DOI":"10.1177\/20552076221110543"},{"key":"e_1_2_13_11_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jbi.2023.104310"},{"key":"e_1_2_13_12_1","doi-asserted-by":"publisher","DOI":"10.1002\/hcs2.44"},{"key":"e_1_2_13_13_1","doi-asserted-by":"publisher","DOI":"10.1161\/STR.0000000000000160"},{"key":"e_1_2_13_14_1","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0234722"},{"key":"e_1_2_13_15_1","doi-asserted-by":"crossref","unstructured":"Erratum in: Stroke 49 no. 3 (2018):e139 https:\/\/doi.org\/10.1161\/STR.0000000000000165.","DOI":"10.1161\/STR.0000000000000165"},{"key":"e_1_2_13_16_1","doi-asserted-by":"publisher","DOI":"10.1161\/strokeaha.120.030150"},{"key":"e_1_2_13_17_1","doi-asserted-by":"publisher","DOI":"10.1161\/strokeaha.118.022844"},{"key":"e_1_2_13_18_1","doi-asserted-by":"publisher","DOI":"10.1161\/strokeaha.119.027981"},{"key":"e_1_2_13_19_1","doi-asserted-by":"publisher","DOI":"10.3389\/fneur.2021.636474"},{"key":"e_1_2_13_20_1","doi-asserted-by":"publisher","DOI":"10.3390\/app10196791"},{"key":"e_1_2_13_21_1","doi-asserted-by":"publisher","DOI":"10.19745\/j.1003-8868.2024136"},{"key":"e_1_2_13_22_1","doi-asserted-by":"publisher","DOI":"10.26599\/tst.2023.9010092"},{"key":"e_1_2_13_23_1","doi-asserted-by":"publisher","DOI":"10.1136\/svn-2020-000457"},{"key":"e_1_2_13_24_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.cmpb.2024.108521"},{"key":"e_1_2_13_25_1","unstructured":"S.LundbergandS.Lee. A Unified Approach to Interpreting Model Predictions. In:Proceedings of the 31st International Conference on Neural Information Processing Systems.ed.Long Beach California USA(2017)."},{"key":"e_1_2_13_26_1","doi-asserted-by":"publisher","DOI":"10.1161\/strokeaha.120.029305"},{"key":"e_1_2_13_27_1","doi-asserted-by":"publisher","DOI":"10.3389\/fcvm.2022.865852"},{"key":"e_1_2_13_28_1","doi-asserted-by":"publisher","DOI":"10.3390\/s22249859"},{"key":"e_1_2_13_29_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10916-019-1517-9"},{"key":"e_1_2_13_30_1","doi-asserted-by":"publisher","DOI":"10.3390\/s21134269"},{"key":"e_1_2_13_31_1","unstructured":"A.Sawan M.Awad R.Qasrawi andM.Sowan\u201cMachine Learning\u2010Based Stroke Disease Diagnosis Using Electroencephalogram (EEG) Signals \u201d2023;18:2847\u20132866 https:\/\/api.semanticscholar.org\/CorpusID:270057943."},{"key":"e_1_2_13_32_1","doi-asserted-by":"publisher","DOI":"10.1109\/access.2021.3109806"},{"key":"e_1_2_13_33_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.artmed.2022.102342"},{"key":"e_1_2_13_34_1","doi-asserted-by":"publisher","DOI":"10.1007\/s11042-020-10043-z"},{"key":"e_1_2_13_35_1","doi-asserted-by":"publisher","DOI":"10.3390\/s20174995"},{"key":"e_1_2_13_36_1","doi-asserted-by":"publisher","DOI":"10.1109\/access.2022.3169284"},{"key":"e_1_2_13_37_1","unstructured":"A.Gupta\u201cStrokeSave: A Novel High\u2010Performance Mobile Application for Stroke Diagnosis Using Deep Learning and Computer Vision \u201d (2019): 1907.05358 https:\/\/doi.org\/10.48550\/arXiv.1907.05358."},{"key":"e_1_2_13_38_1","doi-asserted-by":"publisher","DOI":"10.3390\/s21237784"},{"key":"e_1_2_13_39_1","doi-asserted-by":"publisher","DOI":"10.2196\/jmir.7092"},{"key":"e_1_2_13_40_1","doi-asserted-by":"publisher","DOI":"10.1007\/s11227-021-04209-1"},{"key":"e_1_2_13_41_1","doi-asserted-by":"publisher","DOI":"10.1109\/mwc.001.2000401"}],"container-title":["Health Care Science"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/pdf\/10.1002\/hcs2.70034","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,30]],"date-time":"2025-10-30T15:03:05Z","timestamp":1761836585000},"score":1,"resource":{"primary":{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/10.1002\/hcs2.70034"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,8,26]]},"references-count":40,"journal-issue":{"issue":"5","published-print":{"date-parts":[[2025,10]]}},"alternative-id":["10.1002\/hcs2.70034"],"URL":"https:\/\/doi.org\/10.1002\/hcs2.70034","archive":["Portico"],"relation":{},"ISSN":["2771-1749","2771-1757"],"issn-type":[{"value":"2771-1749","type":"print"},{"value":"2771-1757","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,8,26]]},"assertion":[{"value":"2025-03-13","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2025-04-16","order":2,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2025-08-26","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}