{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,5]],"date-time":"2026-10-05T07:56:20Z","timestamp":1791186980209,"version":"4.1.0"},"reference-count":39,"publisher":"Wiley","issue":"9","license":[{"start":{"date-parts":[[2026,9,16]],"date-time":"2026-09-16T00:00:00Z","timestamp":1789516800000},"content-version":"vor","delay-in-days":15,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"},{"start":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T00:00:00Z","timestamp":1788220800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"funder":[{"DOI":"10.13039\/501100004748","name":"Mashhad University of Medical Sciences","doi-asserted-by":"publisher","award":["4011639"],"award-info":[{"award-number":["4011639"]}],"id":[{"id":"10.13039\/501100004748","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Health Science Reports"],"published-print":{"date-parts":[[2026,9]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background and Aims<\/jats:title>\n                    <jats:p>Hospital length of stay (LOS) is a key indicator for resource allocation, bed management, and discharge planning in orthopedic trauma care. Given the substantial heterogeneity of LOS across fracture types and the limitations of conventional approaches, this study aimed to develop and validate fracture\u2010specific machine learning models for predicting short vs. long hospital stay.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>In this multicenter retrospective prediction\u2010modeling study, routinely collected hospital information system data from three trauma centers were analyzed. From 326,323 diagnosis\u2010level records, a cleaned visit\u2010level cohort was constructed, including 8935 (S42), 16,906 (S52), 8091 (S72), and 15,293 (S82) admissions after applying eligibility criteria and fracture\u2010specific outlier removal. Predictors included age, sex, insurance status, hospital, COVID\u201019 period, and season. LOS was dichotomized using fracture\u2010specific medians. Logistic regression and XGBoost models were developed using stratified 80\/20 splits and further evaluated using temporal validation (earlier vs. later years). Performance was assessed by AUC (with 95% bootstrap confidence intervals), accuracy, sensitivity, positive predictive value (PPV), F1\u2010score, Brier score, calibration (intercept and slope), and decision\u2010curve analysis.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>LOS distributions were right\u2010skewed and differed substantially across fracture groups, with the longest median LOS in S72 (6 days) and the shortest in S52 (1 day). In internal validation, XGBoost showed superior discrimination (AUC: 0.703 [S52], 0.673 [S42], 0.659 [S82], and 0.618 [S72]) compared with logistic regression. Calibration was acceptable (slopes near 1), and Brier scores ranged from 0.218 to 0.238. Temporal validation demonstrated modest performance decline (AUC range: 0.602\u20130.677), indicating limited transportability. Decision\u2010curve analysis showed consistent net benefit across clinically relevant thresholds. Age was the dominant predictor in most groups, while hospital\u2010related variables were prominent in S52.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>Fracture\u2010specific modeling provides a more appropriate framework for LOS prediction than pooled approaches. XGBoost demonstrated consistent improvements over logistic regression with acceptable discrimination, calibration, and clinical utility. However, moderate performance and temporal degradation highlight the need for richer clinical predictors and external validation before routine implementation.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/hsr2.73225","type":"journal-article","created":{"date-parts":[[2026,9,17]],"date-time":"2026-09-17T05:06:29Z","timestamp":1789621589000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Predicting Hospital Length of Stay in Orthopedic Trauma Patients Using Fracture\u2010Specific Machine Learning Models: A Multicenter Retrospective Prediction\u2010Modeling Study"],"prefix":"10.1002","volume":"9","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4639-8072","authenticated-orcid":false,"given":"Parviz","family":"Marouzi","sequence":"first","affiliation":[{"name":"Department of Health Information Technology, School of Paramedical and Rehabilitation Sciences Mashhad University of Medical Sciences Mashhad Iran"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-4872-8374","authenticated-orcid":false,"given":"Amir","family":"Ahmadi","sequence":"additional","affiliation":[{"name":"Department of Health Information Technology, School of Paramedical and Rehabilitation Sciences Mashhad University of Medical Sciences Mashhad Iran"},{"name":"Student Research Committee Mashhad University of Medical Sciences Mashhad Iran"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2214-0077","authenticated-orcid":false,"given":"Seyyedeh Fatemeh","family":"Mousavi Baigi","sequence":"additional","affiliation":[{"name":"Department of Health Information Technology, School of Paramedical and Rehabilitation Sciences Mashhad University of Medical Sciences Mashhad Iran"},{"name":"Student Research Committee Mashhad University of Medical Sciences Mashhad Iran"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8769-9912","authenticated-orcid":false,"given":"Mohammad H.","family":"Ebrahimzadeh","sequence":"additional","affiliation":[{"name":"Orthopedic Research Center, Ghaem Hospital Mashhad University of Medical Sciences Mashhad Iran"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3153-8968","authenticated-orcid":false,"given":"Seyyed Mohammad","family":"Tabatabaei","sequence":"additional","affiliation":[{"name":"Department of Medical Informatics, Faculty of Medicine Mashhad University of Medical Sciences Mashhad Iran"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5456-8505","authenticated-orcid":false,"given":"Masoumeh","family":"Sarbaz","sequence":"additional","affiliation":[{"name":"Department of Health Information Technology, School of Paramedical and Rehabilitation Sciences Mashhad University of Medical Sciences Mashhad Iran"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0351-4675","authenticated-orcid":false,"given":"Khalil","family":"Kimiafar","sequence":"additional","affiliation":[{"name":"Department of Health Information Technology, School of Paramedical and Rehabilitation Sciences Mashhad University of Medical Sciences Mashhad Iran"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2026,9,16]]},"reference":[{"key":"e_1_2_14_2_1","doi-asserted-by":"publisher","DOI":"10.1007\/s11999-008-0411-z"},{"key":"e_1_2_14_3_1","doi-asserted-by":"publisher","DOI":"10.1155\/2014\/708574"},{"key":"e_1_2_14_4_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jamcollsurg.2011.12.013"},{"key":"e_1_2_14_5_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.surg.2020.06.039"},{"key":"e_1_2_14_6_1","first-page":"1023","volume-title":"Proceedings of the 17th IEEE International Conference on Machine Learning and Applications (ICMLA)","author":"Thompson B.","year":"2018"},{"key":"e_1_2_14_7_1","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0133545"},{"key":"e_1_2_14_8_1","doi-asserted-by":"publisher","DOI":"10.1097\/BOT.0000000000000714"},{"key":"e_1_2_14_9_1","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pdig.0000017"},{"key":"e_1_2_14_10_1","doi-asserted-by":"publisher","DOI":"10.1155\/2023\/9673395"},{"key":"e_1_2_14_11_1","doi-asserted-by":"publisher","DOI":"10.1177\/0951484817696212"},{"key":"e_1_2_14_12_1","doi-asserted-by":"publisher","DOI":"10.1002\/hed.26549"},{"key":"e_1_2_14_13_1","doi-asserted-by":"publisher","DOI":"10.1136\/bmj-2023-078378"},{"key":"e_1_2_14_14_1","doi-asserted-by":"publisher","DOI":"10.1136\/bmj-2024-079530"},{"key":"e_1_2_14_15_1","doi-asserted-by":"publisher","DOI":"10.3390\/jcm15031191"},{"key":"e_1_2_14_16_1","doi-asserted-by":"publisher","DOI":"10.3389\/fmed.2026.1732164"},{"key":"e_1_2_14_17_1","doi-asserted-by":"publisher","DOI":"10.3389\/fcvm.2025.1659184"},{"key":"e_1_2_14_18_1","doi-asserted-by":"publisher","DOI":"10.1186\/1472-6947-14-26"},{"key":"e_1_2_14_19_1","doi-asserted-by":"publisher","DOI":"10.1155\/2016\/7035463"},{"key":"e_1_2_14_20_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijcard.2019.04.003"},{"key":"e_1_2_14_21_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.dss.2010.07.006"},{"key":"e_1_2_14_22_1","doi-asserted-by":"publisher","DOI":"10.47176\/mjiri.38.18"},{"key":"e_1_2_14_23_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jcrc.2020.10.013"},{"key":"e_1_2_14_24_1","doi-asserted-by":"publisher","DOI":"10.7416\/ai.2021.2413"},{"key":"e_1_2_14_25_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jcot.2019.02.008"},{"key":"e_1_2_14_26_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijmedinf.2021.104572"},{"key":"e_1_2_14_27_1","doi-asserted-by":"publisher","DOI":"10.3390\/bioengineering9040156"},{"key":"e_1_2_14_28_1","doi-asserted-by":"publisher","DOI":"10.4258\/hir.2013.19.2.121"},{"key":"e_1_2_14_29_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.artmed.2007.04.002"},{"key":"e_1_2_14_30_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2017.02.027"},{"key":"e_1_2_14_31_1","doi-asserted-by":"publisher","DOI":"10.1186\/s12911-022-01871-3"},{"key":"e_1_2_14_32_1","doi-asserted-by":"crossref","unstructured":"J.ZhangandP.YuMachine Learning Methods for Prediction of COVID\u201019 Patient Length of Stay Using Texas PUDF Data. In: Proceedings of Relevant Conference\/Publisher;2023. p.1\u20137.","DOI":"10.1109\/ICECCME57830.2023.10252792"},{"key":"e_1_2_14_33_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.spinee.2020.03.003"},{"key":"e_1_2_14_34_1","doi-asserted-by":"publisher","DOI":"10.5812\/traumamon.20349"},{"key":"e_1_2_14_35_1","doi-asserted-by":"publisher","DOI":"10.14196\/mjiri.32.67"},{"key":"e_1_2_14_36_1","doi-asserted-by":"publisher","DOI":"10.1186\/s13018-024-04567-3"},{"key":"e_1_2_14_37_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.artmed.2015.02.001"},{"key":"e_1_2_14_38_1","doi-asserted-by":"crossref","unstructured":"M.Profeta A. M.Ponsiglione C.Ponsiglione G.Ferrucci C.Giglio andA.BorrelliComparison of Machine Learning Algorithms to Predict Length of Hospital Stay in Patients Undergoing Heart Bypass Surgery. In: Proceedings of Relevant Conference\/Publisher;2021. p.1\u20135.","DOI":"10.1145\/3502060.3503625"},{"key":"e_1_2_14_39_1","doi-asserted-by":"publisher","DOI":"10.1002\/hsr2.1893"},{"issue":"8","key":"e_1_2_14_40_1","first-page":"22","article-title":"Artificial Intelligence\u2010Based Automated International Classification of Diseases Coding: A Systematic Review","volume":"15","author":"Baigi S. F.","year":"2025","journal-title":"Journal of Medical Signals and Sensors"}],"container-title":["Health Science Reports"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/pdf\/10.1002\/hsr2.73225","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/full-xml\/10.1002\/hsr2.73225","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/pdf\/10.1002\/hsr2.73225","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,10,5]],"date-time":"2026-10-05T07:42:44Z","timestamp":1791186164000},"score":1,"resource":{"primary":{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/10.1002\/hsr2.73225"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,9]]},"references-count":39,"journal-issue":{"issue":"9","published-print":{"date-parts":[[2026,9]]}},"alternative-id":["10.1002\/hsr2.73225"],"URL":"https:\/\/doi.org\/10.1002\/hsr2.73225","archive":["Portico"],"relation":{},"ISSN":["2398-8835","2398-8835"],"issn-type":[{"value":"2398-8835","type":"print"},{"value":"2398-8835","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,9]]},"assertion":[{"value":"2025-11-30","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2026-09-04","order":2,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2026-09-16","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}],"article-number":"e73225"}}