{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,16]],"date-time":"2026-07-16T02:32:18Z","timestamp":1784169138746,"version":"3.55.0"},"reference-count":26,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2023,3,22]],"date-time":"2023-03-22T00:00:00Z","timestamp":1679443200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2023,3,22]],"date-time":"2023-03-22T00:00:00Z","timestamp":1679443200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/501100012165","name":"Key Technologies Research and Development Program","doi-asserted-by":"crossref","award":["2018YFB2101100"],"award-info":[{"award-number":["2018YFB2101100"]}],"id":[{"id":"10.13039\/501100012165","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100003787","name":"Natural Science Foundation of Hebei Province","doi-asserted-by":"publisher","award":["G2019202350"],"award-info":[{"award-number":["G2019202350"]}],"id":[{"id":"10.13039\/501100003787","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["BMC Med Inform Decis Mak"],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>The incidence of stroke is a challenge in China, as stroke imposes a heavy burden on families, national health services, social services, and the economy. The length of hospital stay (LOS) is an essential indicator of utilization of medical services and is usually used to assess the efficiency of hospital management and patient quality of care. This study established a prediction model based on a machine learning algorithm to predict ischemic stroke patients\u2019 LOS.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>A total of 18,195 ischemic stroke patients\u2019 electronic medical records and 28 attributes were extracted from electronic medical records in a large comprehensive hospital in China. The prediction of LOS was regarded as a multi classification problem, and LOS was divided into three categories: 1\u20137 days, 8\u201314 days and more than 14 days. After preprocessing the data and feature selection, the XGBoost algorithm was used to build a machine learning model. Ten fold cross-validation was used for model validation. The accuracy (ACC), recall rate (RE) and F1 measure were used to evaluate the performance of the prediction model of LOS of ischemic stroke patients. Finally, the XGBoost algorithm was used to identify and remove irrelevant features by ranking all attributes based on feature importance.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Compared with the naive Bayesian algorithm, logistic region algorithm, decision tree classifier algorithm and ADaBoost classifier algorithm, the XGBoot algorithm has higher ACC, RE and F1 measure. The average ACC, RE and F1 measure were 0.89, 0.89 and 0.89 under the 10-fold cross-validation. According to the analysis of the importance of features, the LOS of ischemic stroke patients was affected by demographic characteristics, past medical history, admission examination features, and operation characteristics. Finally, the features in terms of hemiplegia aphasia, MRS, NIHSS, TIA, Operation or not, coma index etc. were found to be the top features in importance in predicting the LOS of ischemic stroke patients.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>The XGBoost algorithm was an appropriate machine learning method for predicting the LOS of patients with ischemic stroke. Based on the prediction model, an intelligent medical management prediction system could be developed to predict the LOS based on ischemic stroke patients\u2019 electronic medical records.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1186\/s12911-023-02140-4","type":"journal-article","created":{"date-parts":[[2023,3,26]],"date-time":"2023-03-26T17:15:36Z","timestamp":1679850936000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":34,"title":["A study on predicting the length of hospital stay for Chinese patients with ischemic stroke based on the XGBoost algorithm"],"prefix":"10.1186","volume":"23","author":[{"given":"Rui","family":"Chen","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7980-1326","authenticated-orcid":false,"given":"Shengfa","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jie","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dongwei","family":"Guo","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Weijun","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaoying","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Donghua","family":"Tian","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhiyong","family":"Qu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaohua","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2023,3,22]]},"reference":[{"key":"2140_CR1","doi-asserted-by":"crossref","unstructured":"Collaborators GBDCoD. Global, regional, and national age-sex specific mortality for 264 causes of death, 1980-2016: a systematic analysis for the Global Burden of Disease Study 2016. Lancet. 2017;390:1151\u2013210.","DOI":"10.1016\/S0140-6736(17)32152-9"},{"key":"2140_CR2","doi-asserted-by":"publisher","first-page":"140","DOI":"10.1016\/j.scitotenv.2019.02.452","volume":"669","author":"JJ Gao","year":"2019","unstructured":"Gao JJ, Yu F, Xu ZH, Duan J, Cheng Q, Bai LJ, Zhang YW, Wei QN, Yi WZ, Pan RB, Su H. The association between cold spells and admissions of ischemic stroke in Hefei, China: modified by sex and age. Sci Total Environ. 2019;669:140\u20137.","journal-title":"Sci Total Environ"},{"key":"2140_CR3","doi-asserted-by":"publisher","first-page":"1205","DOI":"10.1016\/S0140-6736(14)60596-1","volume":"383","author":"VL Feigin","year":"2014","unstructured":"Feigin VL, Forouzanfar MH, Krishnamurthi R, Mensah GA. Global burden of stroke: an underestimate Reply. Lancet. 2014;383:1205\u20136.","journal-title":"Lancet"},{"key":"2140_CR4","doi-asserted-by":"publisher","first-page":"2429","DOI":"10.1056\/NEJMoa1804492","volume":"379","author":"VL Feigin","year":"2018","unstructured":"Feigin VL, Nguyen G, Cercy K, Johnson CO, Alam T, Parmar PG, Abajobir AA, Abate KH, Abd-Allah F, Abejie AN, et al. Global, regional, and country-specific lifetime risks of stroke, 1990 and 2016. N Engl J Med. 2018;379:2429\u201337.","journal-title":"N Engl J Med"},{"key":"2140_CR5","volume-title":"Annual dataset on national mortality surveillance 2018","author":"XZX Li","year":"2019","unstructured":"Li XZX. Annual dataset on national mortality surveillance 2018. Beijing: China Science and Technology Press; 2019. (in Chinese)."},{"key":"2140_CR6","doi-asserted-by":"publisher","first-page":"259","DOI":"10.1016\/S2214-109X(13)70089-5","volume":"1","author":"RV Krishnamurthi","year":"2013","unstructured":"Krishnamurthi RV, Feigin VL, Forouzanfar MH, Mensah GA, Connor M, Bennett DA, Moran AE, Sacco RL, Anderson LM, Truelsen T, et al. Global and regional burden of first-ever ischaemic and haemorrhagic stroke during 1990\u20132010: findings from the Global Burden of Disease Study 2010. Lancet Glob Health. 2013;1:259\u201381.","journal-title":"Lancet Glob Health"},{"key":"2140_CR7","doi-asserted-by":"publisher","first-page":"5","DOI":"10.1177\/1747493017749084","volume":"13","author":"GA Donnan","year":"2018","unstructured":"Donnan GA. Navigating 2018, a year in stroke! Int J Stroke. 2018;13:5\u20135.","journal-title":"Int J Stroke"},{"key":"2140_CR8","doi-asserted-by":"publisher","first-page":"66","DOI":"10.1371\/journal.pone.0174861","volume":"12","author":"I Lekander","year":"2017","unstructured":"Lekander I, Willers C, von Euler M, Lilja M, Sunnerhagen KS, Pessah-Rasmussen H, Borgstrom F. Relationship between functional disability and costs one and two years post stroke. PLoS ONE. 2017;12:66.","journal-title":"PLoS ONE"},{"key":"2140_CR9","doi-asserted-by":"publisher","first-page":"75","DOI":"10.1161\/CIRCULATIONAHA.116.025250","volume":"135","author":"WZ Wang","year":"2017","unstructured":"Wang WZ, Jiang B, Sun HX, Ru XJ, Sun DL, Wang LH, Wang LM, Jiang Y, Li YC, Wang YL, et al. Prevalence, incidence, and mortality of stroke in China results from a nationwide population-based survey of 480687 adults. Circulation. 2017;135:75.","journal-title":"Circulation"},{"key":"2140_CR10","first-page":"6","volume":"18","author":"SUN Hai-xin WWz","year":"2018","unstructured":"SUN Hai-xin WWz. A nationwide epidemiological sample survey on cerebrovascular disease in China. Chin J Contemp Neurol Neurosurg. 2018;18:6 (in Chinese).","journal-title":"Chin J Contemp Neurol Neurosurg"},{"key":"2140_CR11","first-page":"10","volume":"17","author":"Group RospatiCW","year":"2020","unstructured":"Group RospatiCW. Brief report on stroke prevention and treatment in China. Chin J Cerebrovasc Dis. 2020;17:10 (in Chinese).","journal-title":"Chin J Cerebrovasc Dis"},{"key":"2140_CR12","first-page":"115","volume":"26","author":"XZS Wang","year":"2020","unstructured":"Wang XZS. Hospitalization Expense for Patients with ischemic stroke: based on degree of structural variation and grey correlation. Chin J Rehabil Theory Pract. 2020;26:115\u20139 (in Chinese).","journal-title":"Chin J Rehabil Theory Pract"},{"key":"2140_CR13","first-page":"7","volume":"9","author":"Y Cao","year":"2016","unstructured":"Cao Y, Guan T, Li J, Qu Y, Wang B, Liu Y. Analysis on the hospitalization costs differences and its effects on health outcome among the stroke patients. Chin J Health Policy. 2016;9:7 ((in Chinese)).","journal-title":"Chin J Health Policy"},{"key":"2140_CR14","first-page":"5","volume":"20","author":"S Chen","year":"2014","unstructured":"Chen S, Li P, Lu H, Guo X. Factors related with length of stay for inpatient rehabilitation after stroke. Chin J Rehabil Theory Pract. 2014;20:5 ((in Chinese)).","journal-title":"Chin J Rehabil Theory Pract"},{"key":"2140_CR15","doi-asserted-by":"publisher","first-page":"376","DOI":"10.1016\/j.eswa.2017.02.023","volume":"78","author":"L Turgeman","year":"2017","unstructured":"Turgeman L, May JH, Sciulli R. Insights from a machine learning model for predicting the hospital Length of Stay (LOS) at the time of admission. Expert Syst Appl. 2017;78:376\u201385.","journal-title":"Expert Syst Appl"},{"key":"2140_CR16","doi-asserted-by":"publisher","first-page":"66","DOI":"10.1186\/1472-6947-14-26","volume":"14","author":"EM Carter","year":"2014","unstructured":"Carter EM, Potts HWW. Predicting length of stay from an electronic patient record system: a primary total knee replacement example. BMC Med Inform Decis Mak. 2014;14:66.","journal-title":"BMC Med Inform Decis Mak"},{"key":"2140_CR17","first-page":"4","volume":"7","author":"S Tanuja","year":"2011","unstructured":"Tanuja S, Dinesh Acharya U, Shailesh KR. Comparison of different data mining techniques to predict hospital length of stay. J Pharm Biomed Sci. 2011;7:4.","journal-title":"J Pharm Biomed Sci"},{"key":"2140_CR18","doi-asserted-by":"publisher","first-page":"591","DOI":"10.1016\/j.trc.2017.10.016","volume":"85","author":"JT Ke","year":"2017","unstructured":"Ke JT, Zheng HY, Yang H, Chen XQ. Short-term forecasting of passenger demand under on-demand ride services: a spatio-temporal deep learning approach. Transp Res C Emerg Technol. 2017;85:591\u2013608.","journal-title":"Transp Res C Emerg Technol"},{"key":"2140_CR19","doi-asserted-by":"publisher","first-page":"21020","DOI":"10.1109\/ACCESS.2018.2818678","volume":"6","author":"DH Zhang","year":"2018","unstructured":"Zhang DH, Qian LY, Mao BJ, Huang C, Huang B, Si YL. A data-driven design for fault detection of wind turbines using random forests and XGboost. IEEE Access. 2018;6:21020\u201331.","journal-title":"IEEE Access"},{"key":"2140_CR20","doi-asserted-by":"publisher","first-page":"24","DOI":"10.1016\/j.elerap.2018.08.002","volume":"31","author":"XJ Ma","year":"2018","unstructured":"Ma XJ, Sha JL, Wang DH, Yu YB, Yang Q, Niu XQ. Study on a prediction of P2P network loan default based on the machine learning LightGBM and XGboost algorithms according to different high dimensional data cleaning. Electron Comm Res Appl. 2018;31:24\u201339.","journal-title":"Electron Comm Res Appl"},{"key":"2140_CR21","first-page":"43","volume":"12","author":"Jie Li","year":"2018","unstructured":"Li Jie, Lan Qiaoling, Ma Shihao. Assessment on insurance fraud risk in basic medical insurance in the context of big data. Chin J Health Policy. 2018;12:43\u201350 (in Chinese).","journal-title":"Chin J Health Policy"},{"key":"2140_CR22","doi-asserted-by":"publisher","first-page":"1577","DOI":"10.1177\/1460458219881335","volume":"26","author":"L Luo","year":"2020","unstructured":"Luo L, Li J, Lian S, Zeng X, Sun L, Li C, Huang D, Zhang W. Using machine learning approaches to predict high-cost chronic obstructive pulmonary disease patients in China. Health Inform J. 2020;26:1577\u201398.","journal-title":"Health Inform J"},{"key":"2140_CR23","doi-asserted-by":"publisher","first-page":"128","DOI":"10.1016\/j.jocn.2020.03.004","volume":"75","author":"K Sato","year":"2020","unstructured":"Sato K, Arai N, Takeuchi S. Status epilepticus severity score as a predictor for the length of stay at hospital for acute-phase treatment in convulsive status epilepticus. J Clin Neurosci. 2020;75:128\u201333.","journal-title":"J Clin Neurosci"},{"key":"2140_CR24","doi-asserted-by":"publisher","first-page":"152","DOI":"10.1016\/j.jstrokecerebrovasdis.2012.10.014","volume":"7","author":"YC Huang","year":"2013","unstructured":"Huang YC. The impact factors on the cost and length of stay among acute ischemic stroke. J Stroke Cerebrovas Dis. 2013;7:152\u20138.","journal-title":"J Stroke Cerebrovas Dis"},{"key":"2140_CR25","doi-asserted-by":"publisher","first-page":"140","DOI":"10.1016\/j.ijcard.2019.01.046","volume":"288","author":"TA Daghistani","year":"2020","unstructured":"Daghistani TA, Elshawi R, Sakr S, Ahmed AM, Al-Thwayee A, Al-Mallah MH. Predictors of in-hospital length of stay among cardiac patients: a machine learning approach. Int J Cardiol. 2020;288:140\u20137.","journal-title":"Int J Cardiol"},{"key":"2140_CR26","doi-asserted-by":"publisher","first-page":"S37","DOI":"10.1016\/j.annemergmed.2020.08.011","volume":"76","author":"O Badaki-Makun","year":"2020","unstructured":"Badaki-Makun O, Casella JF, Tackett S, Tao XT, Chamberlain JM. Association of antibiotic choice with hospital length of stay and risk factors for readmission in patients with sickle cell disease and acute chest syndrome: an observational cohort study. Ann Emerg Med. 2020;76:S37\u201345.","journal-title":"Ann Emerg Med"}],"container-title":["BMC Medical Informatics and Decision Making"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12911-023-02140-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1186\/s12911-023-02140-4\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12911-023-02140-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,3,26]],"date-time":"2023-03-26T17:17:40Z","timestamp":1679851060000},"score":1,"resource":{"primary":{"URL":"https:\/\/bmcmedinformdecismak.biomedcentral.com\/articles\/10.1186\/s12911-023-02140-4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,3,22]]},"references-count":26,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2023,12]]}},"alternative-id":["2140"],"URL":"https:\/\/doi.org\/10.1186\/s12911-023-02140-4","relation":{"has-preprint":[{"id-type":"doi","id":"10.21203\/rs.3.rs-344337\/v1","asserted-by":"object"}]},"ISSN":["1472-6947"],"issn-type":[{"value":"1472-6947","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,3,22]]},"assertion":[{"value":"4 January 2021","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"2 March 2023","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"22 March 2023","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 study complied with the Helsinki Declaration of 1975, revised in 2008. As a retrospective study aiming at improving people\u2019s well being, the study was approved and the need for consent to participate was waived both by the School of Social Development and Public Policy of Beijing Normal University Ethics Committee (Protocol Number: SSDPP-HSC 2019004). All analyses were performed on routinely collected anonymized data from the participating institutions.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical approval and consent to participate"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"value":"The authors declare that they have no conflict of interest.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"49"}}