{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,13]],"date-time":"2025-12-13T06:57:15Z","timestamp":1765609035061,"version":"3.37.3"},"reference-count":73,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2023,10,9]],"date-time":"2023-10-09T00:00:00Z","timestamp":1696809600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2023,10,9]],"date-time":"2023-10-09T00:00:00Z","timestamp":1696809600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"name":"the Swedish Carnegie Hero Fund"},{"DOI":"10.13039\/501100008530","name":"Europeiska regionala utvecklingsfonden","doi-asserted-by":"crossref","id":[{"id":"10.13039\/501100008530","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100002835","name":"Chalmers University of Technology","doi-asserted-by":"crossref","id":[{"id":"10.13039\/501100002835","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["BMC Med Inform Decis Mak"],"abstract":"<jats:title>Abstract<\/jats:title><jats:sec>\n                <jats:title>Background<\/jats:title>\n                <jats:p>Providing optimal care for trauma, the leading cause of death for young adults, remains a challenge e.g., due to field triage limitations in assessing a patient\u2019s condition and deciding on transport destination. Data-driven On Scene Injury Severity Prediction (OSISP) models for motor vehicle crashes have shown potential for providing real-time decision support. The objective of this study is therefore to evaluate if an Artificial Intelligence (AI) based clinical decision support system can identify severely injured trauma patients in the prehospital setting.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Methods<\/jats:title>\n                <jats:p>The Swedish Trauma Registry was used to train and validate five models \u2013 Logistic Regression, Random Forest, XGBoost, Support Vector Machine and Artificial Neural Network \u2013 in a stratified 10-fold cross validation setting and hold-out analysis. The models performed binary classification of the New Injury Severity Score and were evaluated using accuracy metrics, area under the receiver operating characteristic curve (AUC) and Precision-Recall curve (AUCPR), and under- and overtriage rates.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Results<\/jats:title>\n                <jats:p>There were 75,602 registrations between 2013\u20132020 and 47,357 (62.6%) remained after eligibility criteria were applied. Models were based on 21 predictors, including injury location. From the clinical outcome, about 40% of patients\u00a0were undertriaged and 46% were overtriaged. Models demonstrated potential for improved triaging and yielded AUC between 0.80\u20130.89 and AUCPR between 0.43\u20130.62.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Conclusions<\/jats:title>\n                <jats:p>AI based OSISP models have potential to provide support during assessment of injury severity. The findings may be used for developing tools to complement field triage protocols, with potential to improve prehospital trauma care and thereby reduce morbidity and mortality for a large patient population.<\/jats:p>\n              <\/jats:sec>","DOI":"10.1186\/s12911-023-02290-5","type":"journal-article","created":{"date-parts":[[2023,10,9]],"date-time":"2023-10-09T09:18:02Z","timestamp":1696843082000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["On Scene Injury Severity Prediction (OSISP) model for trauma developed using the Swedish Trauma Registry"],"prefix":"10.1186","volume":"23","author":[{"given":"Anna","family":"Bakidou","sequence":"first","affiliation":[]},{"given":"Eva-Corina","family":"Caragounis","sequence":"additional","affiliation":[]},{"given":"Magnus","family":"Andersson Hagiwara","sequence":"additional","affiliation":[]},{"given":"Anders","family":"Jonsson","sequence":"additional","affiliation":[]},{"given":"Bengt Arne","family":"Sj\u00f6qvist","sequence":"additional","affiliation":[]},{"given":"Stefan","family":"Candefjord","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,10,9]]},"reference":[{"key":"2290_CR1","volume-title":"PHTLS: Prehospital Trauma Life Support","author":"National Association of Emergency Medical Technicians NAEMT","year":"2020","unstructured":"National Association of Emergency Medical Technicians NAEMT. PHTLS: Prehospital Trauma Life Support. 9th ed. Burlington: Jones and Bartlett Learning; 2020.","edition":"9"},{"key":"2290_CR2","unstructured":"World Health Organization. Injuries and Violence: the Facts 2014. Geneva: World Health Organization; 2014. Available from: https:\/\/apps.who.int\/iris\/handle\/10665\/149798. Cited 2022 Nov 17."},{"key":"2290_CR3","volume-title":"Traumatologi","author":"S Lennquist","year":"2017","unstructured":"Lennquist S. Traumatologi. 2nd ed. Stockholm: Liber; 2017.","edition":"2"},{"issue":"111","key":"2290_CR4","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s13049-018-0579-x","volume":"26","author":"C Magnusson","year":"2018","unstructured":"Magnusson C, Axelsson C, Nilsson L, Str\u00f6ms\u00f6e A, Munters M, Herlitz J, et al. The final assessment and its association with field assessment in patients who were transported by the emergency medical service. Scand J Trauma Resusc Emerg Med. 2018;26(111):1\u201310. https:\/\/doi.org\/10.1186\/s13049-018-0579-x.","journal-title":"Scand J Trauma Resusc Emerg Med"},{"key":"2290_CR5","unstructured":"Sasser SM, Hunt RC, Faul M, Sugerman D, Pearson WS, Dulski T, et al. Guidelines for field triage of injured patients: Recommendations of the national expert panel on field triage, 2011. Atlanta, GA, USA: Centers for Disease Control and Prevention (CDC); 2012. The Morbidity and Mortality Weekly Report (MMWR) Series: Recommendations and Reports 61(1):1\u201320. Available from: https:\/\/pubmed.ncbi.nlm.nih.gov\/22237112\/. Cited 2022 Nov 17."},{"key":"2290_CR6","volume-title":"Resources for Optimal Care of the Injured Patient","author":"American College of Surgeons (ACS)","year":"2014","unstructured":"American College of Surgeons (ACS). Resources for Optimal Care of the Injured Patient. 6th ed. Chicago: American College of Surgeons; 2014.","edition":"6"},{"issue":"4","key":"2290_CR7","doi-asserted-by":"publisher","first-page":"366","DOI":"10.1056\/NEJMsa052049","volume":"354","author":"EJ MacKenzie","year":"2006","unstructured":"MacKenzie EJ, Jurkovich GJ, Frey KP, Scharfstein DO. A national evaluation of the effect of trauma-center care on mortality. N Engl J Med. 2006;354(4):366\u201378. https:\/\/doi.org\/10.1056\/NEJMsa052049.","journal-title":"N Engl J Med"},{"key":"2290_CR8","doi-asserted-by":"publisher","first-page":"13","DOI":"10.1016\/j.eclinm.2018.07.001","volume":"2\u20133","author":"CG Moran","year":"2018","unstructured":"Moran CG, Lecky F, Bouamra O, Lawrence T, Edwards A, Woodford M, et al. Changing the system-major trauma patients and their outcomes in the NHS (England) 2008\u201317. EClinicalMedicine. 2018;2\u20133:13\u201321. https:\/\/doi.org\/10.1016\/j.eclinm.2018.07.001.","journal-title":"EClinicalMedicine"},{"issue":"38","key":"2290_CR9","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s13017-021-00381-0","volume":"16","author":"RJ Alharbi","year":"2021","unstructured":"Alharbi RJ, Shrestha S, Lewis V, Miller C. The effectiveness of trauma care systems at different stages of development in reducing mortality: a systematic review and meta-analysis. World J Emerg Surg. 2021;16(38):1\u201312. https:\/\/doi.org\/10.1186\/s13017-021-00381-0.","journal-title":"World J Emerg Surg"},{"key":"2290_CR10","unstructured":"Socialstyrelsen. Traumav\u00e5rd vid allvarlig h\u00e4ndelse. Stockholm: Socialstyrelsen; 2015. 2015\u201311\u20135. Available from: https:\/\/www.socialstyrelsen.se\/globalassets\/sharepoint-dokument\/artikelkatalog\/ovrigt\/2015-11-5.pdf. Cited 2022 Nov 17."},{"issue":"1","key":"2290_CR11","doi-asserted-by":"publisher","first-page":"525","DOI":"10.1007\/s00068-020-01446-6","volume":"48","author":"S Candefjord","year":"2020","unstructured":"Candefjord S, Asker L, Caragounis EC. Mortality of trauma patients treated at trauma centers compared to non-trauma centers in Sweden: a retrospective study. Eur J Trauma Emerg Surg. 2020;48(1):525\u201336. https:\/\/doi.org\/10.1007\/s00068-020-01446-6.","journal-title":"Eur J Trauma Emerg Surg"},{"issue":"18","key":"2290_CR12","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s13049-019-0593-7","volume":"27","author":"H Fagerlind","year":"2019","unstructured":"Fagerlind H, Harvey L, Candefjord S, Davidsson J, Brown J. Does injury pattern among major road trauma patients influence prehospital transport decisions regardless of the distance to the nearest trauma centre? - a retrospective study. Scand J Trauma Resusc Emerg Med. 2019;27(18):1\u20139. https:\/\/doi.org\/10.1186\/s13049-019-0593-7.","journal-title":"Scand J Trauma Resusc Emerg Med"},{"issue":"4","key":"2290_CR13","doi-asserted-by":"publisher","first-page":"2803","DOI":"10.1007\/s00068-022-01885-3","volume":"48","author":"DJ Trivedi","year":"2022","unstructured":"Trivedi DJ, Bass GA, Forssten MP, Scheufler K-M, Olivecrona M, Cao Y, et al. The significance of direct transportation to a trauma center on survival for severe traumatic brain injury. Eur J Trauma Emerg Surg. 2022;48(4):2803\u201311. https:\/\/doi.org\/10.1007\/s00068-022-01885-3.","journal-title":"Eur J Trauma Emerg Surg"},{"key":"2290_CR14","unstructured":"Landstingens \u00d6msesidiga F\u00f6rs\u00e4kringsbolag (L\u00d6F). Nationella traumalarmskriterier 2017 \u2013 S\u00e4ker Traumav\u00e5rd. Stockholm, Sweden: Landstingens \u00d6msesidiga F\u00f6rs\u00e4kringsbolag; 2017. Available from: https:\/\/lof.se\/filer\/trauma-broschyr.pdf. Cited 2022 Nov 17."},{"key":"2290_CR15","doi-asserted-by":"publisher","unstructured":"Lupton JR, Davis-O\u2019Reilly C, Jungbauer RM, Newgard CD, Fallat ME, Brown JB, et al. Under-triage and over-triage using the field triage guidelines for injured patients: A systematic review. Prehosp Emerg Care. 2022;1\u20138. https:\/\/doi.org\/10.1080\/10903127.2022.2043963.","DOI":"10.1080\/10903127.2022.2043963"},{"issue":"5","key":"2290_CR16","doi-asserted-by":"publisher","first-page":"1044","DOI":"10.1097\/ta.0b013e3181aca144","volume":"68","author":"S Nakahara","year":"2010","unstructured":"Nakahara S, Matsuoka T, Ueno M, Mizushima Y, Ichikawa M, Yokota J, et al. Predictive factors for undertriage among severe blunt trauma patients: what enables them to slip through an established trauma triage protocol? J Trauma. 2010;68(5):1044\u201351. https:\/\/doi.org\/10.1097\/ta.0b013e3181aca144.","journal-title":"J Trauma"},{"issue":"9","key":"2290_CR17","doi-asserted-by":"publisher","first-page":"997","DOI":"10.1016\/j.ajem.2014.05.038","volume":"32","author":"H Xiang","year":"2014","unstructured":"Xiang H, Wheeler KK, Groner JI, Shi J, Haley KJ. Undertriage of major trauma patients in the US emergency departments. Am J Emerg Med. 2014;32(9):997\u20131004. https:\/\/doi.org\/10.1016\/j.ajem.2014.05.038.","journal-title":"Am J Emerg Med"},{"issue":"12","key":"2290_CR18","doi-asserted-by":"publisher","first-page":"e0226518","DOI":"10.1371\/journal.pone.0226518","volume":"14","author":"D Spangler","year":"2019","unstructured":"Spangler D, Hermansson T, Smekal D, Blomberg H. A validation of machine learning-based risk scores in the prehospital setting. PLoS One. 2019;14(12):e0226518. https:\/\/doi.org\/10.1371\/journal.pone.0226518.","journal-title":"PLoS One"},{"issue":"10","key":"2290_CR19","doi-asserted-by":"publisher","first-page":"e0206006","DOI":"10.1371\/journal.pone.0206006","volume":"13","author":"D Kim","year":"2018","unstructured":"Kim D, You S, So S, Lee J, Yook S, Jang DP, et al. A data-driven artificial intelligence model for remote triage in the prehospital environment. PLoS One. 2018;13(10):e0206006. https:\/\/doi.org\/10.1371\/journal.pone.0206006.","journal-title":"PLoS One"},{"issue":"5","key":"2290_CR20","doi-asserted-by":"publisher","first-page":"421","DOI":"10.1001\/jamasurg.2018.4752","volume":"154","author":"EAJ van Rein","year":"2019","unstructured":"van Rein EAJ, van der Sluijs R, Voskens FJ, Lansink KWW, Houwert RM, Lichtveld RA, et al. Development and validation of a prediction model for prehospital triage of trauma patients. JAMA Surg. 2019;154(5):421\u20139. https:\/\/doi.org\/10.1001\/jamasurg.2018.4752.","journal-title":"JAMA Surg"},{"issue":"4","key":"2290_CR21","doi-asserted-by":"publisher","first-page":"373","DOI":"10.1111\/j.1547-5069.2008.00252.x","volume":"40","author":"T Rue","year":"2008","unstructured":"Rue T, Thompson HJ, Rivara FP, Mackenzie EJ, Jurkovich GJ. Managing the common problem of missing data in trauma studies. J Nurs Scholarsh. 2008;40(4):373\u20138. https:\/\/doi.org\/10.1111\/j.1547-5069.2008.00252.x.","journal-title":"J Nurs Scholarsh"},{"key":"2290_CR22","doi-asserted-by":"publisher","first-page":"211","DOI":"10.1016\/j.aap.2015.04.032","volume":"81","author":"R Buendia","year":"2015","unstructured":"Buendia R, Candefjord S, Fagerlind H, B\u00e1lint A, Sj\u00f6qvist BA. On scene injury severity prediction (OSISP) algorithm for car occupants. Accid Anal Prev. 2015;81:211\u20137. https:\/\/doi.org\/10.1016\/j.aap.2015.04.032.","journal-title":"Accid Anal Prev"},{"issue":"sup2","key":"2290_CR23","doi-asserted-by":"publisher","first-page":"190","DOI":"10.1080\/15389588.2015.1057578","volume":"16","author":"S Candefjord","year":"2015","unstructured":"Candefjord S, Buendia R, Fagerlind H, B\u00e1lint A, Wege C, Sj\u00f6qvist BA. On-scene injury severity prediction (OSISP) algorithm for truck occupants. Traffic Inj Prev. 2015;16(sup2):190\u20136. https:\/\/doi.org\/10.1080\/15389588.2015.1057578.","journal-title":"Traffic Inj Prev"},{"key":"2290_CR24","doi-asserted-by":"publisher","unstructured":"Candefjord S, Sheikh Muhammad A, Bangalore P, Buendia R. On scene injury severity prediction (OSISP) machine learning algorithms for motor vehicle crash occupants in US. J Transp Heal 202;22:101124 https:\/\/doi.org\/10.1016\/j.jth.2021.101124.","DOI":"10.1016\/j.jth.2021.101124"},{"key":"2290_CR25","doi-asserted-by":"publisher","unstructured":"Liu B. \u201cWeak AI\u201d is likely to never become \u201cstrong AI\u201d, so what is its greatest value for us?. arXiv:2103.15294 [cs.Al]. 2021;1\u20137. https:\/\/doi.org\/10.48550\/arXiv.2103.15294.","DOI":"10.48550\/arXiv.2103.15294"},{"key":"2290_CR26","unstructured":"The Swedish Trauma Registry (SweTrau). SweTrau | Svenska Traumaregistret. Solna: SweTrau; 2021. Available from: https:\/\/rcsyd.se\/swetrau\/. Cited 2022 Nov 17."},{"key":"2290_CR27","doi-asserted-by":"publisher","DOI":"10.1007\/s10796-021-10146-4","author":"C Trocin","year":"2021","unstructured":"Trocin C, Mikalef P, Papamitsiou Z, Conboy K. Responsible AI for digital health: a synthesis and a research agenda. Inf Syst Front. 2021. https:\/\/doi.org\/10.1007\/s10796-021-10146-4.","journal-title":"Inf Syst Front"},{"issue":"7","key":"2290_CR28","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/1757-7241-16-7","volume":"16","author":"KG Ringdal","year":"2008","unstructured":"Ringdal KG, Coats TJ, Lefering R, Di Bartolomeo S, Steen PA, R\u00f8ise O, et al. The utstein template for uniform reporting of data following major trauma: a joint revision by SCANTEM, TARN, DGU-TR and RITG. Scand J Trauma Resusc Emerg Med. 2008;16(7):1\u201319. https:\/\/doi.org\/10.1186\/1757-7241-16-7.","journal-title":"Scand J Trauma Resusc Emerg Med"},{"key":"2290_CR29","unstructured":"Association for the Advancement of Automotive Medicine. Abbreviated Injury Scale (AIS). Chicago: Association for the Advancement of Automotive Medicine; [date unknown]. Available from: https:\/\/www.aaam.org\/abbreviated-injury-scale-ais\/. Cited 2022 Nov 17."},{"issue":"3","key":"2290_CR30","doi-asserted-by":"publisher","first-page":"187","DOI":"10.1097\/00005373-197403000-00001","volume":"14","author":"SP Baker","year":"1974","unstructured":"Baker SP, O\u2019Neill B, Haddon WJ, Long WB. The injury severity score: a method for describing patients with multiple injuries and evaluating emergency care. J Trauma. 1974;14(3):187\u201396 Available from: https:\/\/journals.lww.com\/jtrauma\/citation\/1974\/03000\/the_injury_severity_score__a_method_for_describing.1.aspx. Cited 2022 Nov 17.","journal-title":"J Trauma"},{"issue":"6","key":"2290_CR31","doi-asserted-by":"publisher","first-page":"922","DOI":"10.1097\/00005373-199712000-00009","volume":"43","author":"T Osler","year":"1997","unstructured":"Osler T, Baker SP, Long W. A modification of the injury severity score that both improves accuracy and scoring. J Trauma. 1997;43(6):922\u20136. https:\/\/doi.org\/10.1097\/00005373-199712000-00009.","journal-title":"J Trauma"},{"issue":"9","key":"2290_CR32","doi-asserted-by":"publisher","first-page":"895","DOI":"10.1111\/j.1553-2712.1996.tb03538.x","volume":"3","author":"NMF Buderer","year":"1996","unstructured":"Buderer NMF. Statistical methodology: I incorporating the prevalence of disease into the sample size calculation for sensitivity and specificity. Acad Emerg Med. 1996;3(9):895\u2013900. https:\/\/doi.org\/10.1111\/j.1553-2712.1996.tb03538.x.","journal-title":"Acad Emerg Med"},{"key":"2290_CR33","unstructured":"The Swedish Trauma Registry (SweTrau). \u00c5rsrapport 2020. Solna: SweTrau; 2021. \u00c5rsrapporter; 2020. Available from: https:\/\/rcsyd.se\/swetrau\/wp-content\/uploads\/sites\/10\/2021\/09\/Arsrapport-SweTrau-2020.pdf. Cited 2022 Nov 17."},{"issue":"2","key":"2290_CR34","doi-asserted-by":"publisher","first-page":"374","DOI":"10.1097\/SLA.0b013e31828b0fae","volume":"259","author":"L Moore","year":"2014","unstructured":"Moore L, Stelfox HT, Turgeon AF, Nathens AB, Le Sage N, \u00c9mond M, et al. Rates, patterns, and determinants of unplanned readmission after traumatic injury: A multicenter cohort study. Ann Surg. 2014;259(2):374\u201380. https:\/\/doi.org\/10.1097\/SLA.0b013e31828b0fae.","journal-title":"Ann Surg"},{"issue":"Supplement 3","key":"2290_CR35","doi-asserted-by":"publisher","first-page":"S93","DOI":"10.1016\/j.injury.2014.08.027","volume":"45","author":"CIA Pape-K\u00f6hler","year":"2014","unstructured":"Pape-K\u00f6hler CIA, Simanski C, Nienaber U, Lefering R. External factors and the incidence of severe trauma: Time, date, season and moon. Injury. 2014;45(Supplement 3):S93\u20139. https:\/\/doi.org\/10.1016\/j.injury.2014.08.027.","journal-title":"Injury"},{"issue":"1","key":"2290_CR36","doi-asserted-by":"publisher","first-page":"28","DOI":"10.1177\/1460408616649217","volume":"19","author":"A Bagher","year":"2017","unstructured":"Bagher A, Todorova L, Andersson L, Wingren C, Ottosson A, Wangefjord S, et al. Analysis of pre-hospital rescue times on mortality in trauma patients in a Scandinavian urban setting. Trauma. 2017;19(1):28\u201334. https:\/\/doi.org\/10.1177\/1460408616649217.","journal-title":"Trauma"},{"key":"2290_CR37","doi-asserted-by":"publisher","first-page":"106053","DOI":"10.1016\/j.aap.2021.106053","volume":"153","author":"A Hosseinzadeh","year":"2021","unstructured":"Hosseinzadeh A, Kluger R. Do EMS times associate with injury severity? Accid Anal Prev. 2021;153:106053. https:\/\/doi.org\/10.1016\/j.aap.2021.106053.","journal-title":"Accid Anal Prev"},{"issue":"1","key":"2290_CR38","doi-asserted-by":"publisher","first-page":"142","DOI":"10.3109\/10903127.2011.614046","volume":"16","author":"IE Blanchard","year":"2012","unstructured":"Blanchard IE, Doig CJ, Hagel BE, Anton AR, Zygun DA, Kortbeek JB, et al. Emergency medical services response time and mortality in an urban setting. Prehosp Emerg Care. 2012;16(1):142\u201351. https:\/\/doi.org\/10.3109\/10903127.2011.614046.","journal-title":"Prehosp Emerg Care"},{"key":"2290_CR39","first-page":"117","volume-title":"Medical Statistics: a Textbook for the Health Sciences","author":"MJ Campbell","year":"2007","unstructured":"Campbell MJ, Walters SJ, Machin D. Chapter 8, Tests for comparing two groups of categorical or continuous data. In: Medical Statistics: a Textbook for the Health Sciences. 4th ed. Chichester: John Wiley and Sons; 2007. p. 117\u201347.","edition":"4"},{"issue":"1","key":"2290_CR40","doi-asserted-by":"publisher","first-page":"52","DOI":"10.1002\/ams2.228","volume":"4","author":"T Suzuki","year":"2017","unstructured":"Suzuki T, Kimura A, Sasaki R, Uemura T. A survival prediction logistic regression models for blunt trauma victims in Japan. Acute Med Surg. 2017;4(1):52\u20136. https:\/\/doi.org\/10.1002\/ams2.228.","journal-title":"Acute Med Surg"},{"issue":"6","key":"2290_CR41","doi-asserted-by":"publisher","first-page":"799","DOI":"10.3390\/jcm8060799","volume":"8","author":"C-S Rau","year":"2019","unstructured":"Rau C-S, Wu S-C, Chuang J-F, Huang C-Y, Liu H-T, Chien P-C, et al. Machine learning models of survival prediction in trauma patients. J Clin Med. 2019;8(6):799. https:\/\/doi.org\/10.3390\/jcm8060799.","journal-title":"J Clin Med"},{"key":"2290_CR42","doi-asserted-by":"publisher","first-page":"369","DOI":"10.1016\/j.jss.2021.09.017","volume":"270","author":"D Lammers","year":"2022","unstructured":"Lammers D, Marenco C, Morte K, Conner J, Williams J, Bax T, et al. Machine learning for military trauma: Novel massive transfusion predictive models in combat zones. J Surg Res. 2022;270:369\u201375. https:\/\/doi.org\/10.1016\/j.jss.2021.09.017.","journal-title":"J Surg Res"},{"issue":"1","key":"2290_CR43","doi-asserted-by":"publisher","first-page":"64","DOI":"10.1186\/s13054-019-2351-7","volume":"23","author":"Y Raita","year":"2019","unstructured":"Raita Y, Goto T, Faridi MK, Brown DFM, Camargo CA, Hasegawa K. Emergency department triage prediction of clinical outcomes using machine learning models. Crit Care. 2019;23(1):64. https:\/\/doi.org\/10.1186\/s13054-019-2351-7.","journal-title":"Crit Care"},{"issue":"3","key":"2290_CR44","doi-asserted-by":"publisher","first-page":"254","DOI":"10.1080\/17453674.2021.1884408","volume":"92","author":"M Siebelt","year":"2021","unstructured":"Siebelt M, Das D, Van Den Moosdijk A, Warren T, Van Der Putten P, Van Der Weegen W. Machine learning algorithms trained with pre-hospital acquired history-taking data can accurately differentiate diagnoses in patients with hip complaints. Acta Orthop. 2021;92(3):254\u20137. https:\/\/doi.org\/10.1080\/17453674.2021.1884408.","journal-title":"Acta Orthop"},{"key":"2290_CR45","doi-asserted-by":"publisher","first-page":"101109","DOI":"10.1016\/j.ienj.2021.101109","volume":"60","author":"R S\u00e1nchez-Salmer\u00f3n","year":"2022","unstructured":"S\u00e1nchez-Salmer\u00f3n R, G\u00f3mez-Urquiza JL, Albend\u00edn-Garc\u00eda L, Correa-Rodr\u00edguez M, Martos-Cabrera MB, Velando-Soriano A, et al. Machine learning methods applied to triage in emergency services: A systematic review. Int Emerg Nurs. 2022;60:101109. https:\/\/doi.org\/10.1016\/j.ienj.2021.101109.","journal-title":"Int Emerg Nurs"},{"key":"2290_CR46","first-page":"149","volume-title":"Medical Statistics: a Textbook for the Health Sciences","author":"MJ Campbell","year":"2007","unstructured":"Campbell MJ, Walters SJ, Machin D. Chapter 9, Correlation, linear and logistic regression. In: Medical Statistics: a Textbook for the Health Sciences. 4th ed. Chichester: John Wiley and Sons; 2007. p. 149\u201380.","edition":"4"},{"key":"2290_CR47","unstructured":"Ho TK. Random decision forests. In: Proceedings of 3rd International Conference on Document Analysis and Recognition. International Conference on Document Analysis and Recognition. August 14\u201316, 1995; Montrea: IEEE; 1995. p. 278\u201382. Available from: https:\/\/ieeexplore.ieee.org\/document\/598994. Cited 2022 Nov 17."},{"key":"2290_CR48","doi-asserted-by":"crossref","unstructured":"Chen T, Guestrin C. XGBoost: A scalable tree boosting system [Internet]. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD '16: The 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. August 13\u201317, 2016; San Francisco: Association for Computing Machinery; 2016. p. 785\u201394. Available from: https:\/\/dl.acm.org\/doi\/10.1145\/2939672.2939785. Cited 2022 Nov 17.","DOI":"10.1145\/2939672.2939785"},{"issue":"3","key":"2290_CR49","doi-asserted-by":"publisher","first-page":"e0118432","DOI":"10.1371\/journal.pone.0118432","volume":"10","author":"T Saito","year":"2015","unstructured":"Saito T, Rehmsmeier M. The precision-recall plot is more informative than the ROC plot when evaluating binary classifiers on imbalanced datasets. PLoS One. 2015;10(3):e0118432. https:\/\/doi.org\/10.1371\/journal.pone.0118432.","journal-title":"PLoS One"},{"issue":"4","key":"2290_CR50","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3343440","volume":"52","author":"H Kaur","year":"2020","unstructured":"Kaur H, Pannu HS, Malhi AK. A systematic review on imbalanced data challenges in machine learning: Applications and solutions. ACM Comput. 2020;52(4):1\u201336. https:\/\/doi.org\/10.1145\/3343440.","journal-title":"ACM Comput"},{"key":"2290_CR51","unstructured":"Boser BE, Guyon IM, Vapnik VN. A training algorithm for optimal margin classifiers. In: Proceedings of the Fifth Annual Workshop on Computational Learning Theory. COLT92: 5th Annual Workshop on Computational Learning. July 27\u201329, 1992; Pittsburgh, Pennsylvani: Association for Computing Machinery; 1992. p. 144\u201352. Available from: https:\/\/dl.acm.org\/doi\/10.1145\/130385.130401.Cited 2022 Nov 17."},{"key":"2290_CR52","unstructured":"Hastie T, Tibshirani R., Friedman, J. The Elements of Statistical Learning. 2nd ed. New York: Springer; 2009. Available from: \nhttps:\/\/link.springer.com\/content\/pdf\/10.1007\/978-0-387-84858-7.pdf. Cited 2022 Nov 17."},{"issue":"1","key":"2290_CR53","doi-asserted-by":"publisher","first-page":"109","DOI":"10.1016\/j.injury.2015.07.003","volume":"47","author":"CS Palmer","year":"2016","unstructured":"Palmer CS, Gabbe BJ, Cameron PA. Defining major trauma using the 2008 abbreviated injury scale. Injury. 2016;47(1):109\u201315. https:\/\/doi.org\/10.1016\/j.injury.2015.07.003.","journal-title":"Injury"},{"key":"2290_CR54","unstructured":"Whitaker IY, Gennari TD, Whitaker AL. The difference between ISS and NISS in a series of trauma patients in Brazil. In: 47th Annual Proceedings of the Association for the Advancement of Automotive Medicine. Association for the Advancement of Automotive Medicine 47th Annual Conference. September 22\u201324, 2003; Lisbon, Portugal. Barrington: Association for the Advancement of Automotive Medicine; 2003. p. 301\u20139. Available from: https:\/\/pubmed.ncbi.nlm.nih.gov\/12941232\/. Cited 2022 Nov 17."},{"issue":"5","key":"2290_CR55","doi-asserted-by":"publisher","first-page":"261","DOI":"10.1016\/j.cjtee.2021.01.006","volume":"24","author":"H Li","year":"2021","unstructured":"Li H, Ma Y-F. New injury severity score (NISS) outperforms injury severity score (ISS) in the evaluation of severe blunt trauma patients. Chin J Traumatol. 2021;24(5):261\u20135. https:\/\/doi.org\/10.1016\/j.cjtee.2021.01.006.","journal-title":"Chin J Traumatol"},{"key":"2290_CR56","unstructured":"Palmer C. Major trauma and the injury severity score - where should we set the bar? In: 51st Annual Proceedings of the Association for the Advancement of Automotive Medicine. Association for the Advancement of Automotive Medicine 47th Annual Conference. October 15\u201317, 2007; Melbourne: Association for the Advancement of Automotive Medicine; 2007. p. 13\u201329. Available from: https:\/\/pubmed.ncbi.nlm.nih.gov\/18184482\/. Cited 2022 Nov 17."},{"issue":"9","key":"2290_CR57","doi-asserted-by":"publisher","first-page":"1641","DOI":"10.1016\/j.injury.2018.03.035","volume":"49","author":"G Shivasabesan","year":"2018","unstructured":"Shivasabesan G, Mitra B, O\u2019Reilly GM. Missing data in trauma registries: A systematic review. Injury. 2018;49(9):1641\u20137. https:\/\/doi.org\/10.1016\/j.injury.2018.03.035.","journal-title":"Injury"},{"issue":"2","key":"2290_CR58","doi-asserted-by":"publisher","first-page":"96","DOI":"10.1136\/ip.2007.017129","volume":"14","author":"B Roudsari","year":"2008","unstructured":"Roudsari B, Field C, Caetano R. Clustered and missing data in the US national trauma data bank: implications for analysis. Inj Prev. 2008;14(2):96\u2013100. https:\/\/doi.org\/10.1136\/ip.2007.017129.","journal-title":"Inj Prev"},{"issue":"2","key":"2290_CR59","doi-asserted-by":"publisher","first-page":"73","DOI":"10.4103\/0974-2700.44774","volume":"2","author":"L Moore","year":"2009","unstructured":"Moore L, Hanley JA, Lavoie A, Turgeon A. Evaluating the validity of multiple imputation for missing physiological data in the national trauma data bank. J Emerg Trauma Shock. 2009;2(2):73\u20139. https:\/\/doi.org\/10.4103\/0974-2700.44774.","journal-title":"J Emerg Trauma Shock"},{"issue":"162","key":"2290_CR60","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s12874-017-0442-1","volume":"17","author":"JC Jakobsen","year":"2017","unstructured":"Jakobsen JC, Gluud C, Wetterslev J, Winkel P. When and how should multiple imputation be used for handling missing data in randomised clinical trials \u2013 a practical guide with flowcharts. BMC Med Res Methodol. 2017;17(162):1\u201310. https:\/\/doi.org\/10.1186\/s12874-017-0442-1.","journal-title":"BMC Med Res Methodol"},{"issue":"10","key":"2290_CR61","doi-asserted-by":"publisher","first-page":"1122","DOI":"10.1111\/j.1553-2712.2010.00887.x","volume":"17","author":"GM O\u2019Reilly","year":"2010","unstructured":"O\u2019Reilly GM, Jolley DJ, Cameron PA, Gabbe B. Missing in action: A case study of the application of methods for dealing with missing data to trauma system benchmarking. Acad Emerg Med. 2010;17(10):1122\u20139. https:\/\/doi.org\/10.1111\/j.1553-2712.2010.00887.x.","journal-title":"Acad Emerg Med"},{"issue":"3","key":"2290_CR62","doi-asserted-by":"publisher","first-page":"314","DOI":"10.1197\/j.aem.2005.09.011","volume":"13","author":"CD Newgard","year":"2006","unstructured":"Newgard CD. The validity of using multiple imputation for missing out-of-hospital data in a state trauma registry. Acad Emerg Med. 2006;13(3):314\u201324. https:\/\/doi.org\/10.1197\/j.aem.2005.09.011.","journal-title":"Acad Emerg Med"},{"issue":"1","key":"2290_CR63","doi-asserted-by":"publisher","first-page":"143","DOI":"10.1097\/SLA.0b013e31818e544b","volume":"249","author":"LG Glance","year":"2009","unstructured":"Glance LG, Osler TM, Mukamel DB, Meredith W, Dick AW. Impact of statistical approaches for handling missing data on trauma center quality. Ann Surg. 2009;249(1):143\u20138. https:\/\/doi.org\/10.1097\/SLA.0b013e31818e544b.","journal-title":"Ann Surg"},{"key":"2290_CR64","doi-asserted-by":"publisher","first-page":"66","DOI":"10.1016\/j.jclinepi.2020.08.014","volume":"128","author":"M Henriksson","year":"2020","unstructured":"Henriksson M, Saulnier DD, Berg J, Gerdin W\u00e4rnberg M. The transfer of clinical prediction models for early trauma care had uncertain effects on mistriage. J Clin Epidemiol. 2020;128:66\u201373. https:\/\/doi.org\/10.1016\/j.jclinepi.2020.08.014.","journal-title":"J Clin Epidemiol"},{"issue":"3","key":"2290_CR65","doi-asserted-by":"publisher","first-page":"1","DOI":"10.18637\/jss.v045.i03","volume":"45","author":"S van Buuren","year":"2011","unstructured":"van Buuren S, Groothuis-Oudshoorn K. mice\u202f: Multivariate imputation by chained equations in R. J Stat Softw. 2011;45(3):1\u201367. https:\/\/doi.org\/10.18637\/jss.v045.i03.","journal-title":"J Stat Softw"},{"key":"2290_CR66","unstructured":"Kohavi RA. A study of cross-validation and bootstrap for accuracy estimation and model selection. In: Proceedings of the 14th International Joint Conference on Artificial Intelligence. The 1995 International Joint Conference on AI. August 20\u201325, 1995; Montreal: Morgan Kaufmann Publishers Inc.; 1995. p. 1137\u201343. Available from: https:\/\/dl.acm.org\/doi\/10.5555\/1643031.1643047. Cited 2022 Nov 17."},{"issue":"9","key":"2290_CR67","doi-asserted-by":"publisher","first-page":"1263","DOI":"10.1109\/TKDE.2008.239","volume":"21","author":"H He","year":"2009","unstructured":"He H, Garcia EA. Learning from imbalanced data. IEEE Trans Knowl Data Eng. 2009;21(9):1263\u201384. https:\/\/doi.org\/10.1109\/TKDE.2008.239.","journal-title":"IEEE Trans Knowl Data Eng"},{"issue":"1","key":"2290_CR68","doi-asserted-by":"publisher","first-page":"49","DOI":"10.1145\/1882471.1882479","volume":"12","author":"G Forman","year":"2010","unstructured":"Forman G, Scholz M. Apples-to-apples in cross-validation studies: pitfalls in classifier performance measurement. SIGKDD Explor Newsl. 2010;12(1):49\u201357. https:\/\/doi.org\/10.1145\/1882471.1882479.","journal-title":"SIGKDD Explor Newsl"},{"issue":"2","key":"2290_CR69","doi-asserted-by":"publisher","first-page":"266","DOI":"10.1097\/CORR.0000000000001484","volume":"479","author":"WF Sherman","year":"2021","unstructured":"Sherman WF, Khadra HS, Kale NN, Wu VJ, Gladden PB, Lee OC. How did the number and type of injuries in patients presenting to a regional level I trauma center change during the COVID-19 pandemic with a stay-at-home order? Clin Orthop Relat Res. 2021;479(2):266\u201375. https:\/\/doi.org\/10.1097\/CORR.0000000000001484.","journal-title":"Clin Orthop Relat Res"},{"issue":"8","key":"2290_CR70","first-page":"553","volume":"5","author":"N Saravanan","year":"2018","unstructured":"Saravanan N, Sathish G, Balajee JM. Data wrangling and data leakage in machine learning for healthcare. J Emerg Technol Innov Res. 2018;5(8):553\u20139 Available from: https:\/\/www.jetir.org\/view?paper=JETIRC006413. Cited 2022 Nov 17.","journal-title":"J Emerg Technol Innov Res"},{"key":"2290_CR71","doi-asserted-by":"publisher","first-page":"220","DOI":"10.1016\/j.eswa.2016.12.035","volume":"73","author":"G Haixiang","year":"2017","unstructured":"Haixiang G, Yijing L, Shang J, Mingyun G, Yuanyue H, Bing G. Learning from class-imbalanced data: Review of methods and applications. Expert Syst Appl. 2017;73:220\u201339. https:\/\/doi.org\/10.1016\/j.eswa.2016.12.035.","journal-title":"Expert Syst Appl"},{"issue":"6","key":"2290_CR72","doi-asserted-by":"publisher","first-page":"2998","DOI":"10.3390\/ijerph18062998","volume":"18","author":"BS Wiratama","year":"2021","unstructured":"Wiratama BS, Chen P-L, Chao C-J, Wang M-H, Saleh W, Lin H-A, et al. Effect of distance to trauma centre, trauma centre level, and trauma centre region on fatal injuries among motorcyclists in Taiwan. Int J Environ Res Public Health. 2021;18(6):2998. https:\/\/doi.org\/10.3390\/ijerph18062998.","journal-title":"Int J Environ Res Public Health"},{"issue":"53","key":"2290_CR73","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s13049-019-0630-6","volume":"27","author":"A Niklasson","year":"2019","unstructured":"Niklasson A, Herlitz J, Jood K. Socioeconomic disparities in prehospital stroke care. Scand J Trauma Resusc Emerg Med. 2019;27(53):1\u20139. https:\/\/doi.org\/10.1186\/s13049-019-0630-6.","journal-title":"Scand J Trauma Resusc Emerg Med"}],"container-title":["BMC Medical Informatics and Decision Making"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12911-023-02290-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1186\/s12911-023-02290-5\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12911-023-02290-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,11,18]],"date-time":"2023-11-18T21:47:17Z","timestamp":1700344037000},"score":1,"resource":{"primary":{"URL":"https:\/\/bmcmedinformdecismak.biomedcentral.com\/articles\/10.1186\/s12911-023-02290-5"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,10,9]]},"references-count":73,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2023,12]]}},"alternative-id":["2290"],"URL":"https:\/\/doi.org\/10.1186\/s12911-023-02290-5","relation":{},"ISSN":["1472-6947"],"issn-type":[{"type":"electronic","value":"1472-6947"}],"subject":[],"published":{"date-parts":[[2023,10,9]]},"assertion":[{"value":"25 November 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"4 September 2023","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"9 October 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 was performed in accordance with relevant guidelines and regulations, including the Declaration of Helsinki. The study was accepted by the Swedish Ethical Review Authority on the 10th of February, 2021 (reference number 2020\u201306899) and conducted in agreement with the ethical references of the Swedish Research Council. The need for informed consent was waived by the Swedish Ethical Review Authority. All registry data were pseudonymized and the dataset did not contain any personal data.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"value":"The authors declare no competing interests.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"206"}}