{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,15]],"date-time":"2026-10-15T11:07:55Z","timestamp":1792062475267,"version":"4.4.0"},"reference-count":20,"publisher":"Elsevier BV","issue":"3","license":[{"start":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T00:00:00Z","timestamp":1775001600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T00:00:00Z","timestamp":1775001600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T00:00:00Z","timestamp":1775001600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T00:00:00Z","timestamp":1775001600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T00:00:00Z","timestamp":1775001600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T00:00:00Z","timestamp":1775001600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T00:00:00Z","timestamp":1775001600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"funder":[{"DOI":"10.13039\/501100005739","name":"Universidad Nacional Autonoma de Mexico","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100005739","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100007487","name":"Universidad Autonoma de la Ciudad de Mexico","doi-asserted-by":"publisher","award":["UACM-CCyT-2023-IMP-05"],"award-info":[{"award-number":["UACM-CCyT-2023-IMP-05"]}],"id":[{"id":"10.13039\/501100007487","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["clinicalkey.com","clinicalkey.com.au","clinicalkey.es","clinicalkey.fr","clinicalkey.jp","elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Infectious Diseases Now"],"published-print":{"date-parts":[[2026,4]]},"DOI":"10.1016\/j.idnow.2026.105267","type":"journal-article","created":{"date-parts":[[2026,4,3]],"date-time":"2026-04-03T15:59:25Z","timestamp":1775231965000},"page":"105267","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":2,"title":["Automated triage in pandemics: Support vector machine for efficient patient classification"],"prefix":"10.1016","volume":"56","author":[{"given":"Ana Gabriela","family":"Gallardo-Hern\u00e1ndez","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tania","family":"Col\u00edn-Mart\u00ednez","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8174-4391","authenticated-orcid":false,"given":"Marcos A.","family":"Gonz\u00e1lez-Olvera","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5875-898X","authenticated-orcid":false,"given":"Jesica","family":"Escobar","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5255-7754","authenticated-orcid":false,"given":"Anahi","family":"Flores-P\u00e9rez","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Debbie","family":"Hern\u00e1ndez","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1485-5504","authenticated-orcid":false,"given":"Eli Abraham","family":"V\u00e1zquez-Ordu\u00f1a","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"issue":"3","key":"10.1016\/j.idnow.2026.105267_b0005","doi-asserted-by":"crossref","first-page":"398","DOI":"10.1111\/jep.13030","article-title":"Validity of the Manchester Triage System in emergency patients receiving life-saving intervention or acute medical treatment\u2014a prospective observational study in the emergency department","volume":"25","author":"Gr\u00e4ff","year":"2019","journal-title":"J Eval Clin Pract"},{"issue":"4","key":"10.1016\/j.idnow.2026.105267_b0010","doi-asserted-by":"crossref","first-page":"312","DOI":"10.5114\/ait.2020.100564","article-title":"Triage during the COVID-19 pandemic","volume":"52","author":"Kucewicz-Czech","year":"2020","journal-title":"Anaesthesiol Intensive Ther"},{"issue":"4","key":"10.1016\/j.idnow.2026.105267_b0015","doi-asserted-by":"crossref","first-page":"11","DOI":"10.7748\/en2013.07.21.4.11.s11","article-title":"Emergency triage","volume":"21","author":"Marsden","year":"2013","journal-title":"Emerg Nurse"},{"issue":"1","key":"10.1016\/j.idnow.2026.105267_b0020","doi-asserted-by":"crossref","first-page":"2990","DOI":"10.1080\/07853890.2022.2136402","article-title":"Machine learning models predicting undertriage in telephone triage","volume":"54","author":"Inokuchi","year":"2022","journal-title":"Ann Med"},{"key":"10.1016\/j.idnow.2026.105267_b0025","series-title":"Support vector machines","author":"Mammone","year":"2008"},{"issue":"8","key":"10.1016\/j.idnow.2026.105267_b0030","first-page":"1583","article-title":"Prediction of COVID-19 corona virus pandemic based on time series data using support Vector Machine","volume":"23","author":"Singh","year":"2020","journal-title":"J Discret Math Sci Cryptogr"},{"key":"10.1016\/j.idnow.2026.105267_b0035","doi-asserted-by":"crossref","unstructured":"Guhathakurata S, Kundu S, Chakraborty A, Banerjee JS. A novel approach to predict COVID-19 using support vector machine. In: Data Science for COVID-19. Elsevier; 2021. p. 351-64.","DOI":"10.1016\/B978-0-12-824536-1.00014-9"},{"key":"10.1016\/j.idnow.2026.105267_b0040","doi-asserted-by":"crossref","unstructured":"Arslan H. Machine learning methods for covid-19 prediction using human genomic data. In: Proceedings. vol. 74. MDPI; 2021. p. 20.","DOI":"10.3390\/proceedings2021074020"},{"issue":"1","key":"10.1016\/j.idnow.2026.105267_b0045","doi-asserted-by":"crossref","first-page":"56","DOI":"10.1186\/s12967-021-02720-w","article-title":"Development and validation of a prognostic COVID-19 severity assess- ment (COSA) score and machine learning models for patient triage at a tertiary hospital","volume":"19","author":"Sch\u00f6ning","year":"2021","journal-title":"J Transl Med"},{"key":"10.1016\/j.idnow.2026.105267_b0050","doi-asserted-by":"crossref","unstructured":"Lansiaux E, Baron MA, Vromant A. Navigating the landscape of medical triage: Unveiling the potential and challenges of large language models and beyond. Am J Emerg Med. 2024;78:224.2024; S0735-6757.","DOI":"10.1016\/j.ajem.2024.02.013"},{"key":"10.1016\/j.idnow.2026.105267_b0055","doi-asserted-by":"crossref","unstructured":"Lansiaux E, Azzouz R, Chazard E, Vromant A, Wiel E. Development and Comparative Evaluation of Three Artificial Intelligence Models (NLP, LLM, JEPA) for Predicting Triage in Emergency Departments: A 7-Month Retrospective Proof-of-Concept. arXiv preprint arXiv:2507.01080; 2025.","DOI":"10.1145\/3773276.3774289"},{"key":"10.1016\/j.idnow.2026.105267_b0060","doi-asserted-by":"crossref","DOI":"10.1016\/j.cie.2024.110103","article-title":"Prediction of Covid-19 confirmed cases and deaths using hybrid support vector machine-Taguchi method","volume":"191","author":"G\u00f6kler","year":"2024","journal-title":"Comput Ind Eng"},{"issue":"1","key":"10.1016\/j.idnow.2026.105267_b0065","first-page":"14","article-title":"Diagnosis of COVID-19 from computed tomography slices using flower pollination algorithm, k-nearest neighbor, and support vector machine classifiers","volume":"2","author":"Betshrine","year":"2025","journal-title":"Art Intellig Health"},{"issue":"3","key":"10.1016\/j.idnow.2026.105267_b0070","doi-asserted-by":"crossref","first-page":"e168","DOI":"10.1016\/S1473-3099(25)00354-8","article-title":"Artificial intelligence and infectious disease diagnostics: state of the art and future perspectives","volume":"26","author":"Miglietta","year":"2026","journal-title":"Lancet Infect Dis"},{"key":"10.1016\/j.idnow.2026.105267_b0075","doi-asserted-by":"crossref","unstructured":"Lansiaux E, Ibrahim E, Minaz A, Itani R, Kukkadi R, Obinna O, et al. Medical Triage: From Battlegrounds Darkness to the Machine Light. 2024. Preprint.","DOI":"10.2139\/ssrn.4713040"},{"key":"10.1016\/j.idnow.2026.105267_b0080","doi-asserted-by":"crossref","unstructured":"Bhuvaneswari E, Prasad KDV, Ashraf M, Jadhav S, Rama Krishna Rao TK, Rani TS. A human-centered hybrid AI framework for optimizing emergency triage in resource-constrained settings.Intelligence-Based Medicine.2025:12;100311.","DOI":"10.1016\/j.ibmed.2025.100311"},{"issue":"1","key":"10.1016\/j.idnow.2026.105267_b0085","doi-asserted-by":"crossref","first-page":"403","DOI":"10.1186\/s12913-025-12535-w","article-title":"Interpretable machine learning models for prolonged Emergency Department wait time prediction","volume":"25","author":"Wang","year":"2025","journal-title":"BMC Health Serv Res"},{"issue":"10","key":"10.1016\/j.idnow.2026.105267_b0090","article-title":"A foundational architecture for AI agents in healthcare","volume":"6","author":"Liu","year":"2025","journal-title":"Cell Rep Med"},{"issue":"7","key":"10.1016\/j.idnow.2026.105267_b0095","doi-asserted-by":"crossref","first-page":"440","DOI":"10.3390\/biomimetics9070440","article-title":"Innovation through artificial intelligence in triage systems for resource optimization in future pandemics","volume":"9","author":"Garrido","year":"2024","journal-title":"Biomimetics"},{"issue":"1","key":"10.1016\/j.idnow.2026.105267_b0100","article-title":"Artificial intelligence (AI) and emergency medicine: balancing opportunities and challenges","volume":"13","author":"Amiot","year":"2025","journal-title":"JMIR Med Inform"}],"container-title":["Infectious Diseases Now"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S2666991926000242?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S2666991926000242?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T03:46:43Z","timestamp":1777607203000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S2666991926000242"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,4]]},"references-count":20,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2026,4]]}},"alternative-id":["S2666991926000242"],"URL":"https:\/\/doi.org\/10.1016\/j.idnow.2026.105267","relation":{},"ISSN":["2666-9919"],"issn-type":[{"value":"2666-9919","type":"print"}],"subject":[],"published":{"date-parts":[[2026,4]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Automated triage in pandemics: Support vector machine for efficient patient classification","name":"articletitle","label":"Article Title"},{"value":"Infectious Diseases Now","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.idnow.2026.105267","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 Elsevier Masson SAS. All rights are reserved, including those for text and data mining, AI training, and similar technologies.","name":"copyright","label":"Copyright"}],"article-number":"105267"}}