{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,9]],"date-time":"2026-03-09T18:50:37Z","timestamp":1773082237552,"version":"3.50.1"},"reference-count":119,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2025,5,15]],"date-time":"2025-05-15T00:00:00Z","timestamp":1747267200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2025,5,15]],"date-time":"2025-05-15T00:00:00Z","timestamp":1747267200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"funder":[{"DOI":"10.13039\/100020595","name":"National Science and Technology Council","doi-asserted-by":"publisher","award":["MOST-110-2410-H-239-015"],"award-info":[{"award-number":["MOST-110-2410-H-239-015"]}],"id":[{"id":"10.13039\/100020595","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["BMC Med Inform Decis Mak"],"DOI":"10.1186\/s12911-025-03010-x","type":"journal-article","created":{"date-parts":[[2025,5,15]],"date-time":"2025-05-15T14:05:20Z","timestamp":1747317920000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["A meta-analysis of the diagnostic test accuracy of artificial intelligence predicting emergency department dispositions"],"prefix":"10.1186","volume":"25","author":[{"given":"Kuang-Ming","family":"Kuo","sequence":"first","affiliation":[]},{"given":"Chao Sheng","family":"Chang","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,5,15]]},"reference":[{"issue":"6","key":"3010_CR1","doi-asserted-by":"publisher","first-page":"359","DOI":"10.1017\/s1481803500002451","volume":"15","author":"A Affleck","year":"2013","unstructured":"Affleck A, Parks P, Drummond A, Rowe BH, Ovens HJ. Emergency department overcrowding and access block. Can J Emerg Med. 2013;15(6):359\u201384. https:\/\/doi.org\/10.1017\/s1481803500002451.","journal-title":"Can J Emerg Med"},{"issue":"2","key":"3010_CR2","doi-asserted-by":"publisher","first-page":"185","DOI":"10.5811\/westjem.2022.10.58045","volume":"24","author":"Y Berlyand","year":"2022","unstructured":"Berlyand Y, Copenhaver MS, White BA, Dutta S, Baugh JJ, Wilcox SR, Yun BJ, Raja AS, Sonis JD. Impact of emergency department crowding on discharged patient experience. Western J Emerg Med. 2022;24(2):185\u201392. https:\/\/doi.org\/10.5811\/westjem.2022.10.58045.","journal-title":"Western J Emerg Med"},{"key":"3010_CR3","doi-asserted-by":"publisher","unstructured":"Parvaresh-Masoud M, Cheraghi MA, Imanipour M. Nurses\u2019 perception of emergency department overcrowding: A qualitative study. J Educ Health Promotion. 2023;12(1). https:\/\/doi.org\/10.4103\/jehp.jehp_1789_22.","DOI":"10.4103\/jehp.jehp_1789_22"},{"issue":"5\u20136","key":"3010_CR4","doi-asserted-by":"publisher","first-page":"e1061","DOI":"10.1111\/jocn.14143","volume":"27","author":"J Eriksson","year":"2018","unstructured":"Eriksson J, Gellerstedt L, Hiller\u00e5s P, Craftman \u00c5sa G. Registered nurses\u2019 perceptions of safe care in overcrowded emergency departments. J Clin Nurs. 2018;27(5\u20136):e1061\u20137. https:\/\/doi.org\/10.1111\/jocn.14143.","journal-title":"J Clin Nurs"},{"key":"3010_CR5","doi-asserted-by":"publisher","first-page":"127","DOI":"10.1016\/j.ajem.2023.05.034","volume":"70","author":"L Azari","year":"2023","unstructured":"Azari L, Turner K, Hong Y-R, Alishahi Tabriz A. Adoption of emergency department crowding interventions among US hospitals between 2007 and 2020. Am J Emerg Med. 2023;70:127\u201332. https:\/\/doi.org\/10.1016\/j.ajem.2023.05.034.","journal-title":"Am J Emerg Med"},{"key":"3010_CR6","doi-asserted-by":"publisher","unstructured":"Lee S, Kang WS, Kim DW, Seo SH, Kim J, Jeong ST, Yon DK, Lee J. An artificial intelligence model for predicting trauma mortality among emergency department patients in South Korea: retrospective cohort study. J Med Internet Res. 2023;25. https:\/\/doi.org\/10.2196\/49283.","DOI":"10.2196\/49283"},{"key":"3010_CR7","doi-asserted-by":"publisher","unstructured":"Chang C-H, Chen C-J, Ma Y-S, Shen Y-T, Sung M-I, Hsu C-C, Lin H-J, Chen Z-C, Huang C-C, Liu C-F. Real-time artificial intelligence predicts adverse outcomes in acute pancreatitis in the emergency department: comparison with clinical decision rule. Academic Emergency Medicine 2023a, n\/a(n\/a). https:\/\/doi.org\/10.1111\/acem.14824","DOI":"10.1111\/acem.14824"},{"key":"3010_CR8","doi-asserted-by":"publisher","unstructured":"Son B, Myung J, Shin Y, Kim S, Kim SH, Chung JM, Noh J, Cho J, Chung HS. Improved patient mortality predictions in emergency departments with deep learning data-synthesis and ensemble models. Sci Rep. 2023;13(1). https:\/\/doi.org\/10.1038\/s41598-023-41544-0.","DOI":"10.1038\/s41598-023-41544-0"},{"issue":"2","key":"3010_CR9","doi-asserted-by":"publisher","first-page":"487","DOI":"10.1007\/s11739-023-03199-7","volume":"18","author":"T Shu","year":"2023","unstructured":"Shu T, Huang J, Deng J, Chen H, Zhang Y, Duan M, Wang Y, Hu X, Liu X. Development and assessment of scoring model for ICU stay and mortality prediction after emergency admissions in ischemic heart disease: a retrospective study of MIMIC-IV databases. Intern Emerg Med. 2023;18(2):487\u201397. https:\/\/doi.org\/10.1007\/s11739-023-03199-7.","journal-title":"Intern Emerg Med"},{"key":"3010_CR10","doi-asserted-by":"crossref","unstructured":"Shahul M, Pushpalatha KP. Machine Learning Based Patient Classification In Emergency Department. In: 2023 International Conference on Advances in Intelligent Computing and Applications. Kochi, India: IEEE; 2023.","DOI":"10.1109\/AICAPS57044.2023.10074003"},{"issue":"8","key":"3010_CR11","doi-asserted-by":"publisher","first-page":"2078","DOI":"10.1007\/s10620-019-05645-z","volume":"64","author":"D Shung","year":"2019","unstructured":"Shung D, Simonov M, Gentry M, Au B, Laine L. Machine learning to predict outcomes in patients with acute Gastrointestinal bleeding: A systematic review. Dig Dis Sci. 2019;64(8):2078\u201387. https:\/\/doi.org\/10.1007\/s10620-019-05645-z.","journal-title":"Dig Dis Sci"},{"issue":"4","key":"3010_CR12","doi-asserted-by":"publisher","first-page":"212","DOI":"10.1007\/s40471-020-00259-w","volume":"7","author":"A Guo","year":"2020","unstructured":"Guo A, Pasque M, Loh F, Mann DL, Payne PRO. Heart failure diagnosis, readmission, and mortality prediction using machine learning and artificial intelligence models. Curr Epidemiol Rep. 2020;7(4):212\u20139. https:\/\/doi.org\/10.1007\/s40471-020-00259-w.","journal-title":"Curr Epidemiol Rep"},{"issue":"2","key":"3010_CR13","doi-asserted-by":"publisher","first-page":"184","DOI":"10.1111\/acem.14190","volume":"28","author":"H Kareemi","year":"2021","unstructured":"Kareemi H, Vaillancourt C, Rosenberg H, Fournier K, Yadav K. Machine learning versus usual care for diagnostic and prognostic prediction in the emergency department: A systematic review. Acad Emerg Med. 2021;28(2):184\u201396. https:\/\/doi.org\/10.1111\/acem.14190.","journal-title":"Acad Emerg Med"},{"issue":"11","key":"3010_CR14","doi-asserted-by":"publisher","first-page":"e052663","DOI":"10.1136\/bmjopen-2021-052663","volume":"11","author":"A Naemi","year":"2021","unstructured":"Naemi A, Schmidt T, Mansourvar M, Naghavi-Behzad M, Ebrahimi A, Wiil UK. Machine learning techniques for mortality prediction in emergency departments: a systematic review. BMJ Open. 2021;11(11):e052663. https:\/\/doi.org\/10.1136\/bmjopen-2021-052663.","journal-title":"BMJ Open"},{"issue":"4","key":"3010_CR15","doi-asserted-by":"publisher","first-page":"355","DOI":"10.1007\/s10654-023-00973-x","volume":"38","author":"C Buttia","year":"2023","unstructured":"Buttia C, Llanaj E, Raeisi-Dehkordi H, Kastrati L, Amiri M, Me\u00e7ani R, Taneri PE, Ochoa SAG, Raguindin PF, Wehrli F, et al. Prognostic models in COVID-19 infection that predict severity: a systematic review. Eur J Epidemiol. 2023;38(4):355\u201372. https:\/\/doi.org\/10.1007\/s10654-023-00973-x.","journal-title":"Eur J Epidemiol"},{"key":"3010_CR16","doi-asserted-by":"publisher","first-page":"166","DOI":"10.1016\/j.ajem.2023.08.043","volume":"73","author":"Y Chen","year":"2023","unstructured":"Chen Y, Chen H, Sun Q, Zhai R, Liu X, Zhou J, Li S. Machine learning model identification and prediction of patients\u2019 need for ICU admission: A systematic review. Am J Emerg Med. 2023;73:166\u201370. https:\/\/doi.org\/10.1016\/j.ajem.2023.08.043.","journal-title":"Am J Emerg Med"},{"issue":"1","key":"3010_CR17","doi-asserted-by":"publisher","first-page":"59","DOI":"10.1186\/s13017-023-00527-2","volume":"18","author":"M Issaiy","year":"2023","unstructured":"Issaiy M, Zarei D, Saghazadeh A. Artificial intelligence and acute appendicitis: A systematic review of diagnostic and prognostic models. World J Emerg Surg. 2023;18(1):59. https:\/\/doi.org\/10.1186\/s13017-023-00527-2.","journal-title":"World J Emerg Surg"},{"issue":"5","key":"3010_CR18","doi-asserted-by":"publisher","first-page":"849","DOI":"10.3390\/jpm13050849","volume":"13","author":"N Larburu","year":"2023","unstructured":"Larburu N, Azkue L, Kerexeta J. Predicting hospital ward admission from the emergency department: A systematic review. J Personalized Med. 2023;13(5):849. https:\/\/doi.org\/10.3390\/jpm13050849.","journal-title":"J Personalized Med"},{"key":"3010_CR19","doi-asserted-by":"publisher","unstructured":"Olender RT, Roy S, Nishtala PS. Application of machine learning approaches in predicting clinical outcomes in older adults \u2013 a systematic review and meta-analysis. BMC Geriatr. 2023;23(1). https:\/\/doi.org\/10.1186\/s12877-023-04246-w.","DOI":"10.1186\/s12877-023-04246-w"},{"key":"3010_CR20","doi-asserted-by":"publisher","unstructured":"Zhang Y, Xu W, Yang P, Zhang A. Machine learning for the prediction of sepsis-related death: a systematic review and meta-analysis. BMC Med Inf Decis Mak. 2023;23(1). https:\/\/doi.org\/10.1186\/s12911-023-02383-1.","DOI":"10.1186\/s12911-023-02383-1"},{"issue":"9","key":"3010_CR21","doi-asserted-by":"publisher","first-page":"1650","DOI":"10.1016\/j.ajem.2018.06.062","volume":"36","author":"T Goto","year":"2018","unstructured":"Goto T, Camargo CA, Faridi MK, Yun BJ, Hasegawa K. Machine learning approaches for predicting disposition of asthma and COPD exacerbations in the ED. Am J Emerg Med. 2018;36(9):1650\u20134. https:\/\/doi.org\/10.1016\/j.ajem.2018.06.062.","journal-title":"Am J Emerg Med"},{"issue":"21","key":"3010_CR22","doi-asserted-by":"publisher","first-page":"14975","DOI":"10.1007\/s00521-021-06133-0","volume":"33","author":"I Ozer","year":"2021","unstructured":"Ozer I, Cetin O, Gorur K, Temurtas F. Improved machine learning performances with transfer learning to predicting need for hospitalization in arboviral infections against the small dataset. Neural Comput Appl. 2021;33(21):14975\u201389. https:\/\/doi.org\/10.1007\/s00521-021-06133-0.","journal-title":"Neural Comput Appl"},{"key":"3010_CR23","doi-asserted-by":"publisher","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). https:\/\/doi.org\/10.1186\/s13054-019-2351-7.","DOI":"10.1186\/s13054-019-2351-7"},{"issue":"1","key":"3010_CR24","doi-asserted-by":"publisher","first-page":"658","DOI":"10.1038\/s41597-022-01782-9","volume":"9","author":"F Xie","year":"2022","unstructured":"Xie F, Zhou J, Lee JW, Tan M, Li S, Rajnthern LSO, Chee ML, Chakraborty B, Wong A-KI, Dagan A, et al. Benchmarking emergency department prediction models with machine learning and public electronic health records. Sci Data. 2022;9(1):658. https:\/\/doi.org\/10.1038\/s41597-022-01782-9.","journal-title":"Sci Data"},{"issue":"3","key":"3010_CR25","doi-asserted-by":"publisher","first-page":"353","DOI":"10.1016\/j.annemergmed.2022.07.026","volume":"81","author":"G Bunney","year":"2023","unstructured":"Bunney G, Tran S, Han S, Gu C, Wang H, Luo Y, Dresden S. Using machine learning to predict hospital disposition with geriatric emergency department innovation intervention. Ann Emerg Med. 2023;81(3):353\u201363. https:\/\/doi.org\/10.1016\/j.annemergmed.2022.07.026.","journal-title":"Ann Emerg Med"},{"key":"3010_CR26","doi-asserted-by":"publisher","first-page":"104146","DOI":"10.1016\/j.ijmedinf.2020.104146","volume":"139","author":"C-H Chen","year":"2020","unstructured":"Chen C-H, Hsieh J-G, Cheng S-L, Lin Y-L, Lin P-H, Jeng J-H. Emergency department disposition prediction using a deep neural network with integrated clinical narratives and structured data. Int J Med Informatics. 2020;139:104146. https:\/\/doi.org\/10.1016\/j.ijmedinf.2020.104146.","journal-title":"Int J Med Informatics"},{"key":"3010_CR27","doi-asserted-by":"crossref","unstructured":"Li J, Guo L, Handly N. Hospital Admission Prediction Using Pre-hospital Variables. In: 2009 IEEE International Conference on Bioinformatics and Biomedicine. 2009: 283\u2013286.","DOI":"10.1109\/BIBM.2009.45"},{"key":"3010_CR28","doi-asserted-by":"publisher","unstructured":"Patel D, Cheetirala SN, Raut G, Tamegue J, Kia A, Glicksberg B, Freeman R, Levin MA, Timsina P, Klang E. Predicting adult hospital admission from emergency department using machine learning: an inclusive gradient boosting model. J Clin Med. 2022;11(23). https:\/\/doi.org\/10.3390\/jcm11236888.","DOI":"10.3390\/jcm11236888"},{"key":"3010_CR29","doi-asserted-by":"publisher","first-page":"170","DOI":"10.1016\/j.neunet.2020.03.012","volume":"126","author":"BP Roquette","year":"2020","unstructured":"Roquette BP, Nagano H, Marujo EC, Maiorano AC. Prediction of admission in pediatric emergency department with deep neural networks and triage textual data. Neural Netw. 2020;126:170\u20137. https:\/\/doi.org\/10.1016\/j.neunet.2020.03.012.","journal-title":"Neural Netw"},{"key":"3010_CR30","doi-asserted-by":"publisher","unstructured":"Akhlaghi H, Freeman S, Vari C, McKenna B, Braitberg G, Karro J, Tahayori B. Machine learning in clinical practice: evaluation of an artificial intelligence tool after implementation. Emerg Med Australasia. 2023;n\/a(n\/a). https:\/\/doi.org\/10.1111\/1742-6723.14325.","DOI":"10.1111\/1742-6723.14325"},{"key":"3010_CR31","doi-asserted-by":"publisher","first-page":"184","DOI":"10.1016\/j.ijmedinf.2019.06.008","volume":"129","author":"NW Sterling","year":"2019","unstructured":"Sterling NW, Patzer RE, Di M, Schrager JD. Prediction of emergency department patient disposition based on natural Language processing of triage notes. Int J Med Informatics. 2019;129:184\u20138. https:\/\/doi.org\/10.1016\/j.ijmedinf.2019.06.008.","journal-title":"Int J Med Informatics"},{"issue":"3","key":"3010_CR32","doi-asserted-by":"publisher","first-page":"480","DOI":"10.1111\/1742-6723.13656","volume":"33","author":"B Tahayori","year":"2021","unstructured":"Tahayori B, Chini-Foroush N, Akhlaghi H. Advanced natural Language processing technique to predict patient disposition based on emergency triage notes. Emerg Med Australasia. 2021;33(3):480\u20134. https:\/\/doi.org\/10.1111\/1742-6723.13656.","journal-title":"Emerg Med Australasia"},{"issue":"1","key":"3010_CR33","doi-asserted-by":"publisher","first-page":"e186937","DOI":"10.1001\/jamanetworkopen.2018.6937","volume":"2","author":"T Goto","year":"2019","unstructured":"Goto T, Camargo CA Jr., Faridi MK, Freishtat RJ, Hasegawa K. Machine Learning\u2013Based prediction of clinical outcomes for children during emergency department triage. JAMA Netw Open. 2019;2(1):e186937\u2013186937. https:\/\/doi.org\/10.1001\/jamanetworkopen.2018.6937.","journal-title":"JAMA Netw Open"},{"issue":"8","key":"3010_CR34","doi-asserted-by":"publisher","first-page":"1736","DOI":"10.1093\/jamia\/ocab076","volume":"28","author":"Y Barak-Corren","year":"2021","unstructured":"Barak-Corren Y, Agarwal I, Michelson KA, Lyons TW, Neuman MI, Lipsett SC, Kimia AA, Eisenberg MA, Capraro AJ, Levy JA, et al. Prediction of patient disposition: comparison of computer and human approaches and a proposed synthesis. J Am Med Inform Assoc. 2021;28(8):1736\u201345. https:\/\/doi.org\/10.1093\/jamia\/ocab076.","journal-title":"J Am Med Inform Assoc"},{"key":"3010_CR35","doi-asserted-by":"publisher","unstructured":"Cusid\u00f3 J, Comalrena J, Alavi H, Llunas L. Predicting hospital admissions to reduce crowding in the emergency departments. Appl Sci (Switzerland). 2022;12(21). https:\/\/doi.org\/10.3390\/app122110764.","DOI":"10.3390\/app122110764"},{"issue":"6","key":"3010_CR36","doi-asserted-by":"publisher","first-page":"738","DOI":"10.1016\/j.annemergmed.2022.11.012","volume":"81","author":"FZ Dadabhoy","year":"2023","unstructured":"Dadabhoy FZ, Driver L, McEvoy DS, Stevens R, Rubins D, Dutta S. Prospective external validation of a commercial model predicting the likelihood of inpatient admission from the emergency department. Ann Emerg Med. 2023;81(6):738\u201348. https:\/\/doi.org\/10.1016\/j.annemergmed.2022.11.012.","journal-title":"Ann Emerg Med"},{"key":"3010_CR37","doi-asserted-by":"publisher","first-page":"104496","DOI":"10.1016\/j.ijmedinf.2021.104496","volume":"152","author":"A De Hond","year":"2021","unstructured":"De Hond A, Raven W, Schinkelshoek L, Gaakeer M, Ter Avest E, Sir O, Lameijer H, Hessels RA, Reijnen R, De Jonge E, et al. Machine learning for developing a prediction model of hospital admission of emergency department patients: hype or hope? Int J Med Informatics. 2021;152:104496. https:\/\/doi.org\/10.1016\/j.ijmedinf.2021.104496.","journal-title":"Int J Med Informatics"},{"key":"3010_CR38","doi-asserted-by":"crossref","unstructured":"Feretzakis G, Sakagianni A, Kalles D, Loupelis E, Panteris V, Tzelves L, Chatzikyriakou R, Trakas N, Kolokytha S, Batiani P et al. Using Machine Learning for Predicting the Hospitalization of Emergency Department Patients. In: Studies in Health Technology and Informatics: 2022; 2022: 405\u2013408.","DOI":"10.3233\/SHTI220751"},{"key":"3010_CR39","doi-asserted-by":"publisher","first-page":"10458","DOI":"10.1109\/ACCESS.2018.2808843","volume":"6","author":"B Graham","year":"2018","unstructured":"Graham B, Bond R, Quinn M, Mulvenna M. Using data mining to predict hospital admissions from the emergency department. IEEE Access. 2018;6:10458\u201369. https:\/\/doi.org\/10.1109\/ACCESS.2018.2808843.","journal-title":"IEEE Access"},{"issue":"1","key":"3010_CR40","doi-asserted-by":"publisher","first-page":"4200","DOI":"10.1038\/s41598-021-83784-y","volume":"11","author":"FS Heldt","year":"2021","unstructured":"Heldt FS, Vizcaychipi MP, Peacock S, Cinelli M, McLachlan L, Andreotti F, Jovanovi\u0107 S, D\u00fcrichen R, Lipunova N, Fletcher RA, et al. Early risk assessment for COVID-19 patients from emergency department data using machine learning. Sci Rep. 2021;11(1):4200. https:\/\/doi.org\/10.1038\/s41598-021-83784-y.","journal-title":"Sci Rep"},{"key":"3010_CR41","doi-asserted-by":"publisher","first-page":"32479","DOI":"10.1109\/ACCESS.2022.3160742","volume":"10","author":"E Kim","year":"2022","unstructured":"Kim E, Han KS, Cheong T, Lee SW, Eun J, Kim SJ. Analysis on benefits and costs of machine Learning-Based early hospitalization prediction. IEEE Access. 2022;10:32479\u201393. https:\/\/doi.org\/10.1109\/ACCESS.2022.3160742.","journal-title":"IEEE Access"},{"issue":"3","key":"3010_CR42","doi-asserted-by":"publisher","first-page":"100554","DOI":"10.1016\/j.hlpt.2021.100554","volume":"10","author":"C Lam","year":"2021","unstructured":"Lam C, Calvert J, Siefkas A, Barnes G, Pellegrini E, Green-Saxena A, Hoffman J, Mao Q, Das R. Personalized stratification of hospitalization risk amidst COVID-19: A machine learning approach. Health Policy Technol. 2021;10(3):100554. https:\/\/doi.org\/10.1016\/j.hlpt.2021.100554.","journal-title":"Health Policy Technol"},{"issue":"11","key":"3010_CR43","doi-asserted-by":"publisher","first-page":"e36933","DOI":"10.2196\/36933","volume":"6","author":"E Logaras","year":"2022","unstructured":"Logaras E, Billis A, Kyparissidis Kokkinidis I, Ketseridou SN, Fourlis A, Tzotzis A, Imprialos K, Doumas M, Bamidis P. Risk assessment of COVID-19 cases in emergency departments and clinics with the use of Real-World data and artificial intelligence: observational study. JMIR Formative Res. 2022;6(11):e36933. https:\/\/doi.org\/10.2196\/36933.","journal-title":"JMIR Formative Res"},{"key":"3010_CR44","doi-asserted-by":"publisher","unstructured":"Luo G, Stone BL, Nkoy FL, He S, Johnson MD. Predicting appropriate hospital admission of emergency department patients with bronchiolitis: secondary analysis. JMIR Med Inf. 2019;7(1). https:\/\/doi.org\/10.2196\/12591.","DOI":"10.2196\/12591"},{"key":"3010_CR45","doi-asserted-by":"publisher","first-page":"104163","DOI":"10.1016\/j.ijmedinf.2020.104163","volume":"140","author":"F Mowbray","year":"2020","unstructured":"Mowbray F, Zargoush M, Jones A, de Wit K, Costa A. Predicting hospital admission for older emergency department patients: insights from machine learning. Int J Med Informatics. 2020;140:104163. https:\/\/doi.org\/10.1016\/j.ijmedinf.2020.104163.","journal-title":"Int J Med Informatics"},{"issue":"12","key":"3010_CR46","doi-asserted-by":"publisher","first-page":"1463","DOI":"10.1111\/acem.13655","volume":"25","author":"SJ Patel","year":"2018","unstructured":"Patel SJ, Chamberlain DB, Chamberlain JM. A machine learning approach to predicting need for hospitalization for pediatric asthma exacerbation at the time of emergency department triage. Acad Emerg Med. 2018;25(12):1463\u201370. https:\/\/doi.org\/10.1111\/acem.13655.","journal-title":"Acad Emerg Med"},{"issue":"1","key":"3010_CR47","doi-asserted-by":"publisher","first-page":"280","DOI":"10.1186\/s12877-021-02229-3","volume":"21","author":"TH Tan","year":"2021","unstructured":"Tan TH, Hsu CC, Chen CJ, Hsu SL, Liu TL, Lin HJ, Wang JJ, Liu CF, Huang CC. Predicting outcomes in older ED patients with influenza in real time using a big data-driven and machine learning approach to the hospital information system. BMC Geriatr. 2021;21(1):280. https:\/\/doi.org\/10.1186\/s12877-021-02229-3.","journal-title":"BMC Geriatr"},{"key":"3010_CR48","doi-asserted-by":"publisher","first-page":"112788","DOI":"10.1016\/j.eswa.2019.07.005","volume":"138","author":"P Wolff","year":"2019","unstructured":"Wolff P, R\u00edos SA, Gra\u00f1a M. Setting up standards: A methodological proposal for pediatric triage machine learning model construction based on clinical outcomes. Expert Syst Appl. 2019;138:112788. https:\/\/doi.org\/10.1016\/j.eswa.2019.07.005.","journal-title":"Expert Syst Appl"},{"key":"3010_CR49","doi-asserted-by":"publisher","unstructured":"Chen M-C, Huang T-Y, Chen T-Y, Boonyarat P, Chang Y-C. Clinical narrative-aware deep neural network for emergency department critical outcome prediction. J Biomedical Inf. 2023;138:104284. https:\/\/doi.org\/10.1016\/j.jbi.2023.104284","DOI":"10.1016\/j.jbi.2023.104284"},{"issue":"4","key":"3010_CR50","doi-asserted-by":"publisher","first-page":"305","DOI":"10.4258\/hir.2019.25.4.305","volume":"25","author":"SW Choi","year":"2019","unstructured":"Choi SW, Ko T, Hong KJ, Kim KH. Machine Learning-Based prediction of Korean triage and acuity scale level in emergency department patients. Healthc Inf Res. 2019;25(4):305\u201312. https:\/\/doi.org\/10.4258\/hir.2019.25.4.305.","journal-title":"Healthc Inf Res"},{"issue":"10","key":"3010_CR51","doi-asserted-by":"publisher","first-page":"1735","DOI":"10.1038\/s41591-021-01506-3","volume":"27","author":"I Dayan","year":"2021","unstructured":"Dayan I, Roth HR, Zhong A, Harouni A, Gentili A, Abidin AZ, Liu A, Costa AB, Wood BJ, Tsai C-S, et al. Federated learning for predicting clinical outcomes in patients with COVID-19. Nat Med. 2021;27(10):1735\u201343. https:\/\/doi.org\/10.1038\/s41591-021-01506-3.","journal-title":"Nat Med"},{"key":"3010_CR52","doi-asserted-by":"publisher","DOI":"10.1007\/s10278-022-00734-4","author":"A Di Napoli","year":"2022","unstructured":"Di Napoli A, Tagliente E, Pasquini L, Cipriano E, Pietrantonio F, Ortis P, Curti S, Boellis A, Stefanini T, Bernardini A, et al. 3D CT-Inclusive Deep-Learning model to predict mortality, ICU admittance, and intubation in COVID-19 patients. J Digit Imaging. 2022. https:\/\/doi.org\/10.1007\/s10278-022-00734-4.","journal-title":"J Digit Imaging"},{"issue":"1","key":"3010_CR53","doi-asserted-by":"publisher","first-page":"10868","DOI":"10.1038\/s41598-023-37512-3","volume":"13","author":"F Dipaola","year":"2023","unstructured":"Dipaola F, Gatti M, Giaj Levra A, Men\u00e8 R, Shiffer D, Faccincani R, Raouf Z, Secchi A, Rovere Querini P, Voza A, et al. Multimodal deep learning for COVID-19 prognosis prediction in the emergency department: a bi-centric study. Sci Rep. 2023;13(1):10868. https:\/\/doi.org\/10.1038\/s41598-023-37512-3.","journal-title":"Sci Rep"},{"key":"3010_CR54","doi-asserted-by":"publisher","unstructured":"Fernandes M, Mendes R, Vieira SM, Leite F, Palos C, Johnson A, Finkelstein S, Horng S, Celi LA. Risk of mortality and cardiopulmonary arrest in critical patients presenting to the emergency department using machine learning and natural Language processing. PLoS ONE. 2020;15(4). https:\/\/doi.org\/10.1371\/journal.pone.0230876.","DOI":"10.1371\/journal.pone.0230876"},{"issue":"3","key":"3010_CR55","doi-asserted-by":"publisher","first-page":"e0229331","DOI":"10.1371\/journal.pone.0229331","volume":"15","author":"M Fernandes","year":"2020","unstructured":"Fernandes M, Mendes R, Vieira SM, Leite F, Palos C, Johnson A, Finkelstein S, Horng S, Celi LA. Predicting intensive care unit admission among patients presenting to the emergency department using machine learning and natural Language processing. PLoS ONE. 2020b;15(3):e0229331. https:\/\/doi.org\/10.1371\/journal.pone.0229331.","journal-title":"PLoS ONE"},{"issue":"5","key":"3010_CR56","doi-asserted-by":"publisher","first-page":"773","DOI":"10.1002\/emp2.12218","volume":"1","author":"JW Joseph","year":"2020","unstructured":"Joseph JW, Leventhal EL, Grossestreuer AV, Wong ML, Joseph LJ, Nathanson LA, Donnino MW, Elhadad N, Sanchez LD. Deep-learning approaches to identify critically ill patients at emergency department triage using limited information. J Am Coll Emerg Physicians Open. 2020;1(5):773\u201381. https:\/\/doi.org\/10.1002\/emp2.12218.","journal-title":"J Am Coll Emerg Physicians Open"},{"key":"3010_CR57","doi-asserted-by":"publisher","unstructured":"Klang E, Kummer BR, Dangayach NS, Zhong A, Kia MA, Timsina P, Cossentino I, Costa AB, Levin MA, Oermann EK. Predicting adult neuroscience intensive care unit admission from emergency department triage using a retrospective, tabular-free text machine learning approach. Sci Rep. 2021;11(1). https:\/\/doi.org\/10.1038\/s41598-021-80985-3.","DOI":"10.1038\/s41598-021-80985-3"},{"key":"3010_CR58","doi-asserted-by":"publisher","first-page":"104662","DOI":"10.1016\/j.ijmedinf.2021.104662","volume":"158","author":"L Butler","year":"2021","unstructured":"Butler L, Karabayir I, Samie Tootooni M, Afshar M, Goldberg A, Akbilgic O. Image and structured data analysis for prognostication of health outcomes in patients presenting to the ED during the COVID-19 pandemic. Int J Med Informatics. 2021;158:104662. https:\/\/doi.org\/10.1016\/j.ijmedinf.2021.104662.","journal-title":"Int J Med Informatics"},{"key":"3010_CR59","doi-asserted-by":"publisher","unstructured":"Kang D-Y, Cho K-J, Kwon O, Kwon J-m, Jeon K-H, Park H, Lee Y, Park J, Oh B-H. Artificial intelligence algorithm to predict the need for critical care in prehospital emergency medical services. Scand J Trauma Resusc Emerg Med. 2020;28(1). https:\/\/doi.org\/10.1186\/s13049-020-0713-4.","DOI":"10.1186\/s13049-020-0713-4"},{"key":"3010_CR60","doi-asserted-by":"publisher","unstructured":"Choi A, Choi SY, Chung K, Chung HS, Song T, Choi B, Kim JH. Development of a machine learning-based clinical decision support system to predict clinical deterioration in patients visiting the emergency department. Sci Rep. 2023;13(1). https:\/\/doi.org\/10.1038\/s41598-023-35617-3.","DOI":"10.1038\/s41598-023-35617-3"},{"key":"3010_CR61","doi-asserted-by":"publisher","unstructured":"Cardosi JD, Shen H, Groner JI, Armstrong M, Xiang H. Machine learning for outcome predictions of patients with trauma during emergency department care. BMJ Health Care Inf. 2021;28(1). https:\/\/doi.org\/10.1136\/bmjhci-2021-100407.","DOI":"10.1136\/bmjhci-2021-100407"},{"key":"3010_CR62","doi-asserted-by":"publisher","unstructured":"Duanmu H, Ren T, Li H, Mehta N, Singer AJ, Levsky JM, Lipton ML, Duong TQ. Deep learning of longitudinal chest X-ray and clinical variables predicts duration on ventilator and mortality in COVID-19 patients. Biomed Eng Online. 2022;21(1). https:\/\/doi.org\/10.1186\/s12938-022-01045-z.","DOI":"10.1186\/s12938-022-01045-z"},{"key":"3010_CR63","doi-asserted-by":"publisher","unstructured":"Klang E, Levin MA, Soffer S, Zebrowski A, Glicksberg BS, Carr BG, McGreevy J, Reich DL, Freeman R. A simple free-text-like method for extracting semi-structured data from electronic health records: exemplified in prediction of in-hospital mortality. Big Data Cogn Comput. 2021;5(3). https:\/\/doi.org\/10.3390\/bdcc5030040.","DOI":"10.3390\/bdcc5030040"},{"key":"3010_CR64","doi-asserted-by":"publisher","unstructured":"Cheng CY, Kung CT, Chen FC, Chiu IM, Lin CHR, Chu CC, Kung CF, Su CM. Machine learning models for predicting in-hospital mortality in patient with sepsis: analysis of vital sign dynamics. Front Med. 2022;9. https:\/\/doi.org\/10.3389\/fmed.2022.964667.","DOI":"10.3389\/fmed.2022.964667"},{"key":"3010_CR65","doi-asserted-by":"publisher","first-page":"106954","DOI":"10.1016\/j.ijsu.2022.106954","volume":"107","author":"P Fransvea","year":"2022","unstructured":"Fransvea P, Fransvea G, Liuzzi P, Sganga G, Mannini A, Costa G. Study and validation of an explainable machine learning-based mortality prediction following emergency surgery in the elderly: A prospective observational study. Int J Surg. 2022;107:106954. https:\/\/doi.org\/10.1016\/j.ijsu.2022.106954.","journal-title":"Int J Surg"},{"key":"3010_CR66","doi-asserted-by":"publisher","unstructured":"Lee S, Kang WS, Seo S, Kim DW, Ko H, Kim J, Lee S, Lee J. Model for predicting In-Hospital mortality of physical trauma patients using artificial intelligence techniques: nationwide Population-Based study in Korea. J Med Internet Res. 2022;24(12). https:\/\/doi.org\/10.2196\/43757.","DOI":"10.2196\/43757"},{"issue":"2","key":"3010_CR67","doi-asserted-by":"publisher","first-page":"379","DOI":"10.1016\/j.jpedsurg.2020.10.021","volume":"56","author":"N Shahi","year":"2021","unstructured":"Shahi N, Shahi AK, Phillips R, Shirek G, Bensard D, Moulton SL. Decision-making in pediatric blunt solid organ injury: A deep learning approach to predict massive transfusion, need for operative management, and mortality risk. J Pediatr Surg. 2021;56(2):379\u201384. https:\/\/doi.org\/10.1016\/j.jpedsurg.2020.10.021.","journal-title":"J Pediatr Surg"},{"key":"3010_CR68","doi-asserted-by":"publisher","first-page":"n71","DOI":"10.1136\/bmj.n71","volume":"372","author":"MJ Page","year":"2021","unstructured":"Page MJ, McKenzie JE, Bossuyt PM, Boutron I, Hoffmann TC, Mulrow CD, Shamseer L, Tetzlaff JM, Akl EA, Brennan SE, et al. The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. BMJ. 2021a;372:n71. https:\/\/doi.org\/10.1136\/bmj.n71.","journal-title":"BMJ"},{"key":"3010_CR69","doi-asserted-by":"publisher","unstructured":"Page MJ, McKenzie JE, Bossuyt PM, Boutron I, Hoffmann TC, Mulrow CD, Shamseer L, Tetzlaff JM, Akl EA, Brennan SE et al. The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. BMJ 2021b, 372:n71. https:\/\/doi.org\/10.1136\/bmj.n71","DOI":"10.1136\/bmj.n71"},{"issue":"5","key":"3010_CR70","doi-asserted-by":"publisher","first-page":"493","DOI":"10.1016\/S0933-3657(96)00356-9","volume":"8","author":"M Hadzikadic","year":"1996","unstructured":"Hadzikadic M, Hakenewerth A, Bohren B, Norton J, Mehta B, Andrews C. Concept formation vs. logistic regression: predicting death in trauma patients. Artif Intell Med. 1996;8(5):493\u2013504. https:\/\/doi.org\/10.1016\/S0933-3657(96)00356-9.","journal-title":"Artif Intell Med"},{"key":"3010_CR71","doi-asserted-by":"publisher","unstructured":"Berikol GB, Yildiz O, \u00d6zcan \u0130T. Diagnosis of acute coronary syndrome with a support vector machine. J Med Syst. 2016;40(4). https:\/\/doi.org\/10.1007\/s10916-016-0432-6.","DOI":"10.1007\/s10916-016-0432-6"},{"key":"3010_CR72","doi-asserted-by":"publisher","first-page":"535","DOI":"10.1016\/j.ijpe.2016.09.020","volume":"182","author":"D Golmohammadi","year":"2016","unstructured":"Golmohammadi D. Predicting hospital admissions to reduce emergency department boarding. Int J Prod Econ. 2016;182:535\u201344. https:\/\/doi.org\/10.1016\/j.ijpe.2016.09.020.","journal-title":"Int J Prod Econ"},{"issue":"4","key":"3010_CR73","doi-asserted-by":"publisher","first-page":"2559","DOI":"10.1007\/s10462-018-9625-3","volume":"52","author":"N Sch\u00fctz","year":"2018","unstructured":"Sch\u00fctz N, Leichtle AB, Riesen K. A comparative study of pattern recognition algorithms for predicting the inpatient mortality risk using routine laboratory measurements. Artif Intell Rev. 2018;52(4):2559\u201373. https:\/\/doi.org\/10.1007\/s10462-018-9625-3.","journal-title":"Artif Intell Rev"},{"key":"3010_CR74","doi-asserted-by":"publisher","unstructured":"Hong JC, Niedzwiecki D, Palta M, Tenenbaum JD. Predicting emergency visits and hospital admissions during radiation and chemoradiation: an internally validated pretreatment machine learning algorithm. JCO Clin Cancer Inf 2018a, 2:1\u201311. https:\/\/doi.org\/10.1200\/cci.18.00037","DOI":"10.1200\/cci.18.00037"},{"key":"3010_CR75","doi-asserted-by":"publisher","unstructured":"Hong WS, Haimovich AD, Taylor RA. Predicting hospital admission at emergency department triage using machine learning. PLoS One. 2018;13(7). https:\/\/doi.org\/10.1371\/journal.pone.0201016","DOI":"10.1371\/journal.pone.0201016"},{"issue":"1","key":"3010_CR76","doi-asserted-by":"publisher","first-page":"220","DOI":"10.1007\/s11606-019-05512-7","volume":"35","author":"M Klug","year":"2019","unstructured":"Klug M, Barash Y, Bechler S, Resheff YS, Tron T, Ironi A, Soffer S, Zimlichman E, Klang E. A gradient boosting machine learning model for predicting early mortality in the emergency department triage: devising a Nine-Point triage score. J Gen Intern Med. 2019;35(1):220\u20137. https:\/\/doi.org\/10.1007\/s11606-019-05512-7.","journal-title":"J Gen Intern Med"},{"issue":"3","key":"3010_CR77","doi-asserted-by":"publisher","first-page":"339","DOI":"10.1007\/s10729-019-09496-y","volume":"23","author":"S-Y Lee","year":"2019","unstructured":"Lee S-Y, Chinnam RB, Dalkiran E, Krupp S, Nauss M. Prediction of emergency department patient disposition decision for proactive resource allocation for admission. Health Care Manag Sci. 2019;23(3):339\u201359. https:\/\/doi.org\/10.1007\/s10729-019-09496-y.","journal-title":"Health Care Manag Sci"},{"issue":"8","key":"3010_CR78","doi-asserted-by":"publisher","first-page":"e15932","DOI":"10.2196\/15932","volume":"8","author":"S Hong","year":"2020","unstructured":"Hong S, Lee S, Lee J, Cha WC, Kim K. Prediction of cardiac arrest in the emergency department based on machine learning and sequential characteristics: model development and retrospective clinical validation study. JMIR Med Inf. 2020;8(8):e15932. https:\/\/doi.org\/10.2196\/15932.","journal-title":"JMIR Med Inf"},{"key":"3010_CR79","doi-asserted-by":"publisher","unstructured":"Lee S, Hong S, Cha WC, Kim K. Predicting adverse outcomes for febrile patients in the emergency department using sparse laboratory data: development of a time adaptive model. JMIR Med Inf. 2020;8(3). https:\/\/doi.org\/10.2196\/16117.","DOI":"10.2196\/16117"},{"key":"3010_CR80","doi-asserted-by":"publisher","unstructured":"Zhang P-I, Hsu C-C, Kao Y, Chen C-J, Kuo Y-W, Hsu S-L, Liu T-L, Lin H-J, Wang J-J, Liu C-F, et al. Real-time AI prediction for major adverse cardiac events in emergency department patients with chest pain. Scand J Trauma Resusc Emerg Med. 2020;28(1). https:\/\/doi.org\/10.1186\/s13049-020-00786-x.","DOI":"10.1186\/s13049-020-00786-x"},{"issue":"11","key":"3010_CR81","doi-asserted-by":"publisher","first-page":"1277","DOI":"10.1111\/acem.14339","volume":"28","author":"Y-M Chen","year":"2021","unstructured":"Chen Y-M, Kao Y, Hsu C-C, Chen C-J, Ma Y-S, Shen Y-T, Liu T-L, Hsu S-L, Lin H-J, Wang J-J, et al. Real-time interactive artificial intelligence of things\u2013based prediction for adverse outcomes in adult patients with pneumonia in the emergency department. Acad Emerg Med. 2021;28(11):1277\u201385. https:\/\/doi.org\/10.1111\/acem.14339.","journal-title":"Acad Emerg Med"},{"key":"3010_CR82","doi-asserted-by":"publisher","unstructured":"Chiu IM, Cheng CY, Zeng WH, Huang YH, Lin CR. Using machine learning to predict invasive bacterial infections in young febrile infants visiting the emergency department. J Clin Med. 2021;10(9). https:\/\/doi.org\/10.3390\/jcm10091875.","DOI":"10.3390\/jcm10091875"},{"issue":"3","key":"3010_CR83","doi-asserted-by":"publisher","first-page":"578","DOI":"10.1016\/j.bja.2020.11.034","volume":"126","author":"NJ Douville","year":"2021","unstructured":"Douville NJ, Douville CB, Mentz G, Mathis MR, Pancaro C, Tremper KK, Engoren M. Clinically applicable approach for predicting mechanical ventilation in patients with COVID-19. Br J Anaesth. 2021;126(3):578\u201389. https:\/\/doi.org\/10.1016\/j.bja.2020.11.034.","journal-title":"Br J Anaesth"},{"key":"3010_CR84","doi-asserted-by":"publisher","unstructured":"Garrafa E, Vezzoli M, Ravanelli M, Farina D, Borghesi A, Calza S, Maroldi R. Early prediction of in-hospital death of covid-19 patients: A machine-learning model based on age, blood analyses, and chest x-ray score. eLife. 2021;10. https:\/\/doi.org\/10.7554\/eLife.70640.","DOI":"10.7554\/eLife.70640"},{"issue":"2","key":"3010_CR85","doi-asserted-by":"publisher","first-page":"1741","DOI":"10.1007\/s11277-021-08532-x","volume":"120","author":"G-J Horng","year":"2021","unstructured":"Horng G-J, Lin T-C, Lee K-C, Chen K-T, Hsu C-C. Prediction of prognosis in emergency trauma patients with optimal limit gradient based on grid search optimal parameters. Wireless Pers Commun. 2021;120(2):1741\u201351. https:\/\/doi.org\/10.1007\/s11277-021-08532-x.","journal-title":"Wireless Pers Commun"},{"key":"3010_CR86","doi-asserted-by":"publisher","unstructured":"Hsu SD, Chao E, Chen SJ, Hueng DY, Lan HY, Chiang HH. Machine learning algorithms to predict in-hospital mortality in patients with traumatic brain injury. J Personalized Med. 2021;11(11). https:\/\/doi.org\/10.3390\/jpm11111144.","DOI":"10.3390\/jpm11111144"},{"key":"3010_CR87","doi-asserted-by":"publisher","first-page":"104326","DOI":"10.1016\/j.ijmedinf.2020.104326","volume":"145","author":"H Jiang","year":"2021","unstructured":"Jiang H, Mao H, Lu H, Lin P, Garry W, Lu H, Yang G, Rainer TH, Chen X. Machine learning-based models to support decision-making in emergency department triage for patients with suspected cardiovascular disease. Int J Med Informatics. 2021;145:104326. https:\/\/doi.org\/10.1016\/j.ijmedinf.2020.104326.","journal-title":"Int J Med Informatics"},{"key":"3010_CR88","doi-asserted-by":"publisher","first-page":"104570","DOI":"10.1016\/j.ijmedinf.2021.104570","volume":"155","author":"C Li","year":"2021","unstructured":"Li C, Zhang Z, Ren Y, Nie H, Lei Y, Qiu H, Xu Z, Pu X. Machine learning based early mortality prediction in the emergency department. Int J Med Informatics. 2021;155:104570. https:\/\/doi.org\/10.1016\/j.ijmedinf.2021.104570.","journal-title":"Int J Med Informatics"},{"key":"3010_CR89","doi-asserted-by":"publisher","unstructured":"Lu JQ, Musheyev B, Peng Q, Duong TQ. Neural network analysis of clinical variables predicts escalated care in COVID-19 patients: A retrospective study. PeerJ. 2021;9. https:\/\/doi.org\/10.7717\/peerj.11205.","DOI":"10.7717\/peerj.11205"},{"key":"3010_CR90","doi-asserted-by":"publisher","unstructured":"Wu TT, Zheng RF, Lin ZZ, Gong HR, Li H. A machine learning model to predict critical care outcomes in patient with chest pain visiting the emergency department. BMC Emerg Med. 2021;21(1). https:\/\/doi.org\/10.1186\/s12873-021-00501-8.","DOI":"10.1186\/s12873-021-00501-8"},{"key":"3010_CR91","doi-asserted-by":"publisher","unstructured":"Yun H, Choi J, Park JH. Prediction of critical care outcome for adult patients presenting to emergency department using initial triage information: an XGBoost algorithm analysis. JMIR Med Inf. 2021;9(9). https:\/\/doi.org\/10.2196\/30770.","DOI":"10.2196\/30770"},{"key":"3010_CR92","doi-asserted-by":"publisher","unstructured":"Lee JT, Hsieh CC, Lin CH, Lin YJ, Kao CY. Prediction of hospitalization using artificial intelligence for urgent patients in the emergency department. Sci Rep. 2021;11(1). https:\/\/doi.org\/10.1038\/s41598-021-98961-2.","DOI":"10.1038\/s41598-021-98961-2"},{"issue":"3","key":"3010_CR93","doi-asserted-by":"publisher","first-page":"214","DOI":"10.1016\/j.rxeng.2021.09.004","volume":"64","author":"P Calvillo-Batll\u00e9s","year":"2022","unstructured":"Calvillo-Batll\u00e9s P, Cerd\u00e1-Alberich L, Fonfr\u00eda-Esparcia C, Carreres-Ortega A, Mu\u00f1oz-N\u00fa\u00f1ez CF, Trilles-Olaso L, Mart\u00ed-Bonmat\u00ed L. Development of severity and mortality prediction models for covid-19 patients at emergency department including the chest x-ray. Radiologia. 2022;64(3):214\u201327. https:\/\/doi.org\/10.1016\/j.rxeng.2021.09.004.","journal-title":"Radiologia"},{"key":"3010_CR94","doi-asserted-by":"publisher","unstructured":"Chang Y-H, Shih H-M, Wu J-E, Huang F-W, Chen W-K, Chen D-M, Chung Y-T, Wang CCN. Machine learning\u2013based triage to identify low-severity patients with a short discharge length of stay in emergency department. BMC Emerg Med. 2022;22(1). https:\/\/doi.org\/10.1186\/s12873-022-00632-6.","DOI":"10.1186\/s12873-022-00632-6"},{"key":"3010_CR95","doi-asserted-by":"publisher","unstructured":"Hasan M, Bath PA, Marincowitz C, Sutton L, Pilbery R, Hopfgartner F, Mazumdar S, Campbell R, Stone T, Thomas B, et al. Pre-hospital prediction of adverse outcomes in patients with suspected COVID-19: development, application and comparison of machine learning and deep learning methods. Comput Biol Med. 2022;151. https:\/\/doi.org\/10.1016\/j.compbiomed.2022.106024.","DOI":"10.1016\/j.compbiomed.2022.106024"},{"key":"3010_CR96","doi-asserted-by":"publisher","first-page":"127","DOI":"10.1016\/j.ajem.2021.12.070","volume":"53","author":"J Ke","year":"2022","unstructured":"Ke J, Chen Y, Wang X, Wu Z, Zhang Q, Lian Y, Chen F. Machine learning-based in-hospital mortality prediction models for patients with acute coronary syndrome. Am J Emerg Med. 2022;53:127\u201334. https:\/\/doi.org\/10.1016\/j.ajem.2021.12.070.","journal-title":"Am J Emerg Med"},{"key":"3010_CR97","doi-asserted-by":"crossref","unstructured":"Maria A, Dimitrios V, Ioanna M, Charalampos M, Gerasimos M, Constantinos K. Clinical Decision Making and Outcome Prediction for COVID-19 Patients Using Machine Learning. In: 15th EAI International Conference, Pervasive Health 2021. Virtual Event; 2022.","DOI":"10.1007\/978-3-030-99194-4_1"},{"key":"3010_CR98","doi-asserted-by":"publisher","unstructured":"Casano N, Santini SJ, Vittorini P, Sinatti G, Carducci P, Mastroianni CM, Ciardi MR, Pasculli P, Petrucci E, Marinangeli F, et al. Application of machine learning approach in emergency department to support clinical decision making for SARS-CoV-2 infected patients. J Integr Bioinform. 2023;20(2). https:\/\/doi.org\/10.1515\/jib-2022-0047.","DOI":"10.1515\/jib-2022-0047"},{"key":"3010_CR99","doi-asserted-by":"publisher","first-page":"100281","DOI":"10.1016\/j.array.2023.100281","volume":"17","author":"H Elhaj","year":"2023","unstructured":"Elhaj H, Achour N, Tania MH, Aciksari K. A comparative study of supervised machine learning approaches to predict patient triage outcomes in hospital emergency departments. Array. 2023;17:100281. https:\/\/doi.org\/10.1016\/j.array.2023.100281.","journal-title":"Array"},{"key":"3010_CR100","doi-asserted-by":"publisher","unstructured":"Greco M, Caruso PF, Spano S, Citterio G, Desai A, Molteni A, Aceto R, Costantini E, Voza A, Cecconi M. Machine learning for early outcome prediction in septic patients in the emergency department. Algorithms. 2023;16(2). https:\/\/doi.org\/10.3390\/a16020076.","DOI":"10.3390\/a16020076"},{"key":"3010_CR101","doi-asserted-by":"publisher","unstructured":"Hsu C-C, Kao Y, Hsu C-C, Chen C-J, Hsu S-L, Liu T-L, Lin H-J, Wang J-J, Liu C-F, Huang C-C. Using artificial intelligence to predict adverse outcomes in emergency department patients with hyperglycemic crises in real time. BMC Endocr Disorders. 2023;23(1). https:\/\/doi.org\/10.1186\/s12902-023-01437-9.","DOI":"10.1186\/s12902-023-01437-9"},{"key":"3010_CR102","doi-asserted-by":"publisher","DOI":"10.1089\/neu.2022.0515","author":"K Matsuo","year":"2023","unstructured":"Matsuo K, Aihara H, Hara Y, Morishita A, Sakagami Y, Miyake S, Tatsumi S, Ishihara S, Tohma Y, Yamashita H, et al. Machine learning to predict three types of outcomes after traumatic brain injury using data at admission: A Multi-Center study for development and validation. J Neurotrauma. 2023. https:\/\/doi.org\/10.1089\/neu.2022.0515.","journal-title":"J Neurotrauma"},{"key":"3010_CR103","doi-asserted-by":"publisher","unstructured":"Mekkodathil A, El-Menyar A, Naduvilekandy M, Rizoli S, Al-Thani H. Machine learning approach for the prediction of In-Hospital mortality in traumatic brain injury using Bio-Clinical markers at presentation to the emergency department. Diagnostics. 2023;13(15). https:\/\/doi.org\/10.3390\/diagnostics13152605.","DOI":"10.3390\/diagnostics13152605"},{"issue":"1","key":"3010_CR104","doi-asserted-by":"publisher","first-page":"219","DOI":"10.1007\/s11739-022-03100-y","volume":"18","author":"DR Pai","year":"2023","unstructured":"Pai DR, Rajan B, Jairath P, Rosito SM. Predicting hospital admission from emergency department triage data for patients presenting with fall-related fractures. Intern Emerg Med. 2023;18(1):219\u201327. https:\/\/doi.org\/10.1007\/s11739-022-03100-y.","journal-title":"Intern Emerg Med"},{"key":"3010_CR105","doi-asserted-by":"publisher","unstructured":"Logothetis SB, Green D, Holland M, Al Moubayed N. Predicting acute clinical deterioration with interpretable machine learning to support emergency care decision making. Sci Rep. 2023;13(1). https:\/\/doi.org\/10.1038\/s41598-023-40661-0.","DOI":"10.1038\/s41598-023-40661-0"},{"issue":"1","key":"3010_CR106","doi-asserted-by":"publisher","first-page":"W1","DOI":"10.7326\/M18-1377","volume":"170","author":"KGM Moons","year":"2019","unstructured":"Moons KGM, Wolff RF, Riley RD, Whiting PF, Westwood M, Collins GS, Reitsma JB, Kleijnen J, Mallett S. PROBAST: A tool to assess risk of Bias and applicability of prediction model studies: explanation and elaboration. Ann Intern Med. 2019;170(1):W1\u201333. https:\/\/doi.org\/10.7326\/M18-1377.","journal-title":"Ann Intern Med"},{"issue":"1","key":"3010_CR107","doi-asserted-by":"publisher","first-page":"51","DOI":"10.7326\/M18-1376","volume":"170","author":"RF Wolff","year":"2019","unstructured":"Wolff RF, Moons KGM, Riley RD, Whiting PF, Westwood M, Collins GS, Reitsma JB, Kleijnen J, Mallett S. PROBAST: A tool to assess the risk of Bias and applicability of prediction model studies. Ann Intern Med. 2019;170(1):51\u20138. https:\/\/doi.org\/10.7326\/M18-1376.","journal-title":"Ann Intern Med"},{"issue":"4","key":"3010_CR108","doi-asserted-by":"publisher","first-page":"103","DOI":"10.1136\/eb-2015-102228","volume":"18","author":"Y Takwoingi","year":"2015","unstructured":"Takwoingi Y, Riley RD, Deeks JJ. Meta-analysis of diagnostic accuracy studies in mental health. Evid Based Mental Health. 2015;18(4):103. https:\/\/doi.org\/10.1136\/eb-2015-102228.","journal-title":"Evid Based Mental Health"},{"key":"3010_CR109","volume-title":"R: A Language and environment for statistical computing","year":"2023","unstructured":"R Core Team. In: Vienna, editor. R: A Language and environment for statistical computing. Austria: R Foundation for Statistical Computing; 2023."},{"key":"3010_CR110","doi-asserted-by":"publisher","unstructured":"Bates D, M\u00e4chler M, Bolker B, Walker S. Fitting linear Mixed-Effects models using lme4. J Stat Softw. 2015;67. https:\/\/doi.org\/10.18637\/jss.v067.i01.","DOI":"10.18637\/jss.v067.i01"},{"key":"3010_CR111","unstructured":"Doebler P. mada: Meta-Analysis of Diagnostic Accuracy. In R package version 0.5.9. edn; 2019."},{"issue":"1","key":"3010_CR112","doi-asserted-by":"publisher","first-page":"81","DOI":"10.1186\/s12874-019-0724-x","volume":"19","author":"SC Freeman","year":"2019","unstructured":"Freeman SC, Kerby CR, Patel A, Cooper NJ, Quinn T, Sutton AJ. Development of an interactive web-based tool to conduct and interrogate meta-analysis of diagnostic test accuracy studies: MetaDTA. BMC Med Res Methodol. 2019;19(1):81. https:\/\/doi.org\/10.1186\/s12874-019-0724-x.","journal-title":"BMC Med Res Methodol"},{"issue":"1","key":"3010_CR113","doi-asserted-by":"publisher","first-page":"34","DOI":"10.1002\/jrsm.1439","volume":"12","author":"A Patel","year":"2021","unstructured":"Patel A, Cooper N, Freeman S, Sutton A. Graphical enhancements to summary receiver operating characteristic plots to facilitate the analysis and reporting of meta-analysis of diagnostic test accuracy data. Res Synthesis Methods. 2021;12(1):34\u201344. https:\/\/doi.org\/10.1002\/jrsm.1439.","journal-title":"Res Synthesis Methods"},{"issue":"11","key":"3010_CR114","doi-asserted-by":"publisher","first-page":"1129","DOI":"10.1016\/S0895-4356(03)00177-X","volume":"56","author":"AS Glas","year":"2003","unstructured":"Glas AS, Lijmer JG, Prins MH, Bonsel GJ, Bossuyt PMM. The diagnostic odds ratio: a single indicator of test performance. J Clin Epidemiol. 2003;56(11):1129\u201335. https:\/\/doi.org\/10.1016\/S0895-4356(03)00177-X.","journal-title":"J Clin Epidemiol"},{"issue":"7458","key":"3010_CR115","doi-asserted-by":"publisher","first-page":"168","DOI":"10.1136\/bmj.329.7458.168","volume":"329","author":"JJ Deeks","year":"2004","unstructured":"Deeks JJ, Altman DG. Diagnostic tests 4: likelihood ratios. BMJ. 2004;329(7458):168\u20139. https:\/\/doi.org\/10.1136\/bmj.329.7458.168.","journal-title":"BMJ"},{"issue":"7","key":"3010_CR116","doi-asserted-by":"publisher","first-page":"938","DOI":"10.1016\/S1470-2045(19)30333-X","volume":"20","author":"P Tschandl","year":"2019","unstructured":"Tschandl P, Codella N, Akay BN, Argenziano G, Braun RP, Cabo H, Gutman D, Halpern A, Helba B, Hofmann-Wellenhof R, et al. Comparison of the accuracy of human readers versus machine-learning algorithms for pigmented skin lesion classification: an open, web-based, international, diagnostic study. Lancet Oncol. 2019;20(7):938\u201347. https:\/\/doi.org\/10.1016\/S1470-2045(19)30333-X.","journal-title":"Lancet Oncol"},{"key":"3010_CR117","volume-title":"Ensemble learning algorithms with Python","author":"J Brownlee","year":"2020","unstructured":"Brownlee J. Ensemble learning algorithms with Python. Melbourne, Australia: Machine Learning Mastery; 2020."},{"issue":"1","key":"3010_CR118","doi-asserted-by":"publisher","first-page":"13","DOI":"10.1186\/s41512-022-00126-w","volume":"6","author":"P Dhiman","year":"2022","unstructured":"Dhiman P, Ma J, Andaur Navarro CL, Speich B, Bullock G, Damen JAA, Hooft L, Kirtley S, Riley RD, Van Calster B, et al. Risk of bias of prognostic models developed using machine learning: a systematic review in oncology. Diagn Prognostic Res. 2022;6(1):13. https:\/\/doi.org\/10.1186\/s41512-022-00126-w.","journal-title":"Diagn Prognostic Res"},{"key":"3010_CR119","doi-asserted-by":"publisher","first-page":"m1328","DOI":"10.1136\/bmj.m1328","volume":"369","author":"L Wynants","year":"2020","unstructured":"Wynants L, Van Calster B, Collins GS, Riley RD, Heinze G, Schuit E, Albu E, Arshi B, Bellou V, Bonten MMJ, et al. Prediction models for diagnosis and prognosis of covid-19: systematic review and critical appraisal. BMJ. 2020;369:m1328. https:\/\/doi.org\/10.1136\/bmj.m1328.","journal-title":"BMJ"}],"container-title":["BMC Medical Informatics and Decision Making"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12911-025-03010-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1186\/s12911-025-03010-x\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12911-025-03010-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,5,15]],"date-time":"2025-05-15T14:05:30Z","timestamp":1747317930000},"score":1,"resource":{"primary":{"URL":"https:\/\/bmcmedinformdecismak.biomedcentral.com\/articles\/10.1186\/s12911-025-03010-x"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,5,15]]},"references-count":119,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2025,12]]}},"alternative-id":["3010"],"URL":"https:\/\/doi.org\/10.1186\/s12911-025-03010-x","relation":{},"ISSN":["1472-6947"],"issn-type":[{"value":"1472-6947","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,5,15]]},"assertion":[{"value":"8 April 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"23 April 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"15 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":"All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and\/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards. This article does not contain any studies with animals performed by any of the authors. The experimental protocols, approved by the Institutional Review Board of E-DA Hospital (IRB No. EMRP-109-158), included waived informed-consent requirements.","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":"187"}}