{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,9]],"date-time":"2026-09-09T11:15:24Z","timestamp":1788952524172,"version":"build-2803163510"},"reference-count":50,"publisher":"Elsevier BV","issue":"3","license":[{"start":{"date-parts":[[2026,3,1]],"date-time":"2026-03-01T00:00:00Z","timestamp":1772323200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,3,1]],"date-time":"2026-03-01T00:00:00Z","timestamp":1772323200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,1,27]],"date-time":"2026-01-27T00:00:00Z","timestamp":1769472000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["82072295"],"award-info":[{"award-number":["82072295"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2022YFF1203201"],"award-info":[{"award-number":["2022YFF1203201"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["cell.com","elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["iScience"],"published-print":{"date-parts":[[2026,3]]},"DOI":"10.1016\/j.isci.2026.114843","type":"journal-article","created":{"date-parts":[[2026,1,30]],"date-time":"2026-01-30T00:42:48Z","timestamp":1769733768000},"page":"114843","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":1,"title":["Machine learning models for early mortality prediction in severe fever with thrombocytopenia syndrome"],"prefix":"10.1016","volume":"29","author":[{"given":"Chenxi","family":"Zhao","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tingyu","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ziruo","family":"Ge","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ling","family":"Lin","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Di","family":"Tian","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yi","family":"Shen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhenghua","family":"Zhao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jingxia","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jianming","family":"Lai","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yanli","family":"Xu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jianping","family":"Duan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6481-4781","authenticated-orcid":false,"given":"Zhihai","family":"Chen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.isci.2026.114843_bib1","doi-asserted-by":"crossref","first-page":"1523","DOI":"10.1056\/NEJMoa1010095","article-title":"Fever with thrombocytopenia associated with a novel bunyavirus in China","volume":"364","author":"Yu","year":"2011","journal-title":"N. Engl. J. Med."},{"key":"10.1016\/j.isci.2026.114843_bib2","doi-asserted-by":"crossref","first-page":"1892","DOI":"10.3201\/eid1911.130792","article-title":"Severe fever with thrombocytopenia syndrome, South Korea, 2012","volume":"19","author":"Kim","year":"2013","journal-title":"Emerg. Infect. Dis."},{"key":"10.1016\/j.isci.2026.114843_bib3","doi-asserted-by":"crossref","first-page":"692","DOI":"10.3201\/eid2604.191011","article-title":"Severe Fever with Thrombocytopenia Syndrome, Japan, 2013-2017","volume":"26","author":"Kobayashi","year":"2020","journal-title":"Emerg. Infect. Dis."},{"key":"10.1016\/j.isci.2026.114843_bib4","doi-asserted-by":"crossref","first-page":"1029","DOI":"10.3201\/eid2505.181463","article-title":"Endemic Severe Fever with Thrombocytopenia Syndrome, Vietnam","volume":"25","author":"Tran","year":"2019","journal-title":"Emerg. Infect. Dis."},{"key":"10.1016\/j.isci.2026.114843_bib5","doi-asserted-by":"crossref","first-page":"2572","DOI":"10.3201\/eid2812.221183","article-title":"Severe Fever with Thrombocytopenia Syndrome Virus Infection, Thailand, 2019-2020","volume":"28","author":"Rattanakomol","year":"2022","journal-title":"Emerg. Infect. Dis."},{"key":"10.1016\/j.isci.2026.114843_bib6","doi-asserted-by":"crossref","first-page":"1513","DOI":"10.3201\/eid2607.190611","article-title":"Serologic Evidence of Severe Fever with Thrombocytopenia Syndrome Virus and Related Viruses in Pakistan","volume":"26","author":"Zohaib","year":"2020","journal-title":"Emerg. Infect. Dis."},{"key":"10.1016\/j.isci.2026.114843_bib7","doi-asserted-by":"crossref","first-page":"363","DOI":"10.3201\/eid2802.211532","article-title":"Rapid Spread of Severe Fever with Thrombocytopenia Syndrome Virus by Parthenogenetic Asian Longhorned Ticks","volume":"28","author":"Zhang","year":"2022","journal-title":"Emerg. Infect. Dis."},{"key":"10.1016\/j.isci.2026.114843_bib8","doi-asserted-by":"crossref","first-page":"80","DOI":"10.1016\/j.ijid.2022.08.008","article-title":"Human-to-human transmission of severe fever with thrombocytopenia syndrome virus through potential ocular exposure to infectious blood","volume":"123","author":"Wu","year":"2022","journal-title":"Int. J. Infect. Dis."},{"key":"10.1016\/j.isci.2026.114843_bib9","doi-asserted-by":"crossref","first-page":"633.e1","DOI":"10.1016\/j.cmi.2019.01.006","article-title":"Nosocomial person-to-person transmission of severe fever with thrombocytopenia syndrome","volume":"25","author":"Jung","year":"2019","journal-title":"Clin. Microbiol. Infect."},{"key":"10.1016\/j.isci.2026.114843_bib10","doi-asserted-by":"crossref","first-page":"90","DOI":"10.1002\/rmv.1776","article-title":"Systematic review of severe fever with thrombocytopenia syndrome:virology, epidemiology, and clinical characteristics","volume":"24","author":"Liu","year":"2014","journal-title":"Rev. Med. Virol."},{"key":"10.1016\/j.isci.2026.114843_bib11","doi-asserted-by":"crossref","first-page":"1095","DOI":"10.1093\/infdis\/jis472","article-title":"Clinical progress and risk factors for death in severe fever with thrombocytopenia syndrome patients","volume":"206","author":"Gai","year":"2012","journal-title":"J. Infect. Dis."},{"key":"10.1016\/j.isci.2026.114843_bib12","article-title":"Global epidemiology of severe fever with thrombocytopenia syndrome virus in human and animals: a systematic review and meta-analysis","volume":"48","author":"Cui","year":"2024","journal-title":"Lancet Reg. Health West. Pac."},{"key":"10.1016\/j.isci.2026.114843_bib13","doi-asserted-by":"crossref","first-page":"1127","DOI":"10.1016\/S1473-3099(18)30293-7","article-title":"Epidemiological and clinical features of laboratory-diagnosed severe fever with thrombocytopenia syndrome in China, 2011\u201317: a prospective observational study","volume":"18","author":"Li","year":"2018","journal-title":"Lancet Infect. Dis."},{"key":"10.1016\/j.isci.2026.114843_bib14","doi-asserted-by":"crossref","DOI":"10.1016\/j.ebiom.2021.103591","article-title":"Clinical efficacy and safety evaluation of favipiravir in treating patients with severe fever with thrombocytopenia syndrome","volume":"72","author":"Yuan","year":"2021","journal-title":"EBioMedicine"},{"key":"10.1016\/j.isci.2026.114843_bib15","doi-asserted-by":"crossref","first-page":"40","DOI":"10.1016\/j.imj.2021.10.001","article-title":"Epidemiology, clinical characteristics, and treatment of severe fever with thrombocytopenia syndrome","volume":"1","author":"Li","year":"2022","journal-title":"Infect. Med."},{"key":"10.1016\/j.isci.2026.114843_bib16","doi-asserted-by":"crossref","first-page":"10","DOI":"10.1016\/j.ijid.2022.10.008","article-title":"Fatal patients with severe fever with thrombocytopenia syndrome in China","volume":"125","author":"Zhao","year":"2022","journal-title":"Int. J. Infect. Dis."},{"key":"10.1016\/j.isci.2026.114843_bib17","doi-asserted-by":"crossref","DOI":"10.1002\/jmv.70266","article-title":"Autoantibody Profiles and Prognostic Significance in Severe Fever With Thrombocytopenia Syndrome (SFTS) Patients","volume":"97","author":"Hou","year":"2025","journal-title":"J. Med. Virol."},{"key":"10.1016\/j.isci.2026.114843_bib18","doi-asserted-by":"crossref","first-page":"996","DOI":"10.1186\/s12879-024-09867-z","article-title":"Construction and validation of a dynamic nomogram using Lasso-logistic regression for predicting the severity of severe fever with thrombocytopenia syndrome patients at admission","volume":"24","author":"Xia","year":"2024","journal-title":"BMC Infect. Dis."},{"key":"10.1016\/j.isci.2026.114843_bib19","doi-asserted-by":"crossref","first-page":"92","DOI":"10.1016\/j.cell.2020.03.022","article-title":"How Machine Learning will Transform Biomedicine","volume":"181","author":"Goecks","year":"2020","journal-title":"Cell"},{"key":"10.1016\/j.isci.2026.114843_bib20","doi-asserted-by":"crossref","first-page":"e272","DOI":"10.1016\/S2666-5247(23)00363-4","article-title":"Host transcriptomics and machine learning for secondary bacterial infections in patients with COVID-19: a prospective, observational cohort study","volume":"5","author":"Carney","year":"2024","journal-title":"Lancet Microbe"},{"key":"10.1016\/j.isci.2026.114843_bib21","doi-asserted-by":"crossref","first-page":"294","DOI":"10.1016\/j.chom.2024.12.016","article-title":"Decoding the blueprint of receptor binding by filoviruses through large-scale binding assays and machine learning","volume":"33","author":"Lasso","year":"2025","journal-title":"Cell Host Microbe"},{"key":"10.1016\/j.isci.2026.114843_bib22","article-title":"Predicting ICU Readmission in Acute Pancreatitis Patients Using a Machine Learning-Based Model with Enhanced Clinical Interpretability","author":"Chen","year":"2025","journal-title":"arXiv"},{"key":"10.1016\/j.isci.2026.114843_bib23","article-title":"Clinically Interpretable Mortality Prediction for ICU Patients with Diabetes and Atrial Fibrillation: A Machine Learning Approach","author":"Sun","year":"2025","journal-title":"arXiv"},{"key":"10.1016\/j.isci.2026.114843_bib24","doi-asserted-by":"crossref","DOI":"10.1136\/bmj-2023-078276","article-title":"Developing clinical prediction models: a step-by-step guide","volume":"386","author":"Efthimiou","year":"2024","journal-title":"BMJ"},{"key":"10.1016\/j.isci.2026.114843_bib25","doi-asserted-by":"crossref","first-page":"3917","DOI":"10.1007\/s00167-022-07155-4","article-title":"The development and deployment of machine learning models","volume":"30","author":"Pruneski","year":"2022","journal-title":"Knee Surg. Sports Traumatol. Arthrosc."},{"key":"10.1016\/j.isci.2026.114843_bib26","doi-asserted-by":"crossref","DOI":"10.1136\/bmj-2023-078378","article-title":"TRIPOD+AI statement: updated guidance for reporting clinical prediction models that use regression or machine learning methods","volume":"385","author":"Collins","year":"2024","journal-title":"BMJ"},{"key":"10.1016\/j.isci.2026.114843_bib27","doi-asserted-by":"crossref","first-page":"603","DOI":"10.1111\/joim.12822","article-title":"eDoctor: machine learning and the future of medicine","volume":"284","author":"Handelman","year":"2018","journal-title":"J. Intern. Med."},{"key":"10.1016\/j.isci.2026.114843_bib28","doi-asserted-by":"crossref","first-page":"325","DOI":"10.1109\/RBME.2021.3069213","article-title":"A Survey on Mathematical, Machine Learning and Deep Learning Models for COVID-19 Transmission and Diagnosis","volume":"15","author":"Clement","year":"2022","journal-title":"IEEE Rev. Biomed. Eng."},{"key":"10.1016\/j.isci.2026.114843_bib29","doi-asserted-by":"crossref","first-page":"287","DOI":"10.1016\/j.jinf.2023.07.006","article-title":"Artificial intelligence, machine learning and deep learning: Potential resources for the infection clinician","volume":"87","author":"Theodosiou","year":"2023","journal-title":"J. Infect."},{"key":"10.1016\/j.isci.2026.114843_bib30","doi-asserted-by":"crossref","DOI":"10.1016\/j.ijmedinf.2021.104679","article-title":"Potential applications and performance of machine learning techniques and algorithms in clinical practice: A systematic review","volume":"159","author":"Nwanosike","year":"2022","journal-title":"Int. J. Med. Inform."},{"key":"10.1016\/j.isci.2026.114843_bib31","doi-asserted-by":"crossref","DOI":"10.1016\/j.patcog.2021.108197","article-title":"A Unified Hierarchical XGBoost model for classifying priorities for COVID-19 vaccination campaign","volume":"121","author":"Romeo","year":"2022","journal-title":"Pattern Recognit."},{"key":"10.1016\/j.isci.2026.114843_bib32","doi-asserted-by":"crossref","first-page":"1158","DOI":"10.2215\/CJN.17311120","article-title":"Predictive Approaches for Acute Dialysis Requirement and Death in COVID-19. Clin","volume":"16","author":"Vaid","year":"2021","journal-title":"Clin. J. Am. Soc. Nephrol."},{"key":"10.1016\/j.isci.2026.114843_bib33","doi-asserted-by":"crossref","first-page":"349","DOI":"10.1186\/s13054-024-05138-0","article-title":"Interpretable machine learning model for new-onset atrial fibrillation prediction in critically ill patients: a multi-center study","volume":"28","author":"Guan","year":"2024","journal-title":"Crit. Care"},{"key":"10.1016\/j.isci.2026.114843_bib34","doi-asserted-by":"crossref","first-page":"801","DOI":"10.1016\/S2213-2600(18)30425-9","article-title":"Opening the black box of machine learning","volume":"6","author":"Medicine","year":"2018","journal-title":"Lancet Respir. Med."},{"key":"10.1016\/j.isci.2026.114843_bib35","doi-asserted-by":"crossref","first-page":"56","DOI":"10.1038\/s42256-019-0138-9","article-title":"From Local Explanations to Global Understanding with Explainable AI for Trees","volume":"2","author":"Lundberg","year":"2020","journal-title":"Nat. Mach. Intell."},{"key":"10.1016\/j.isci.2026.114843_bib36","doi-asserted-by":"crossref","first-page":"508","DOI":"10.1186\/s12879-025-10900-y","article-title":"Clinical characteristics and outcomes of disseminated intravascular coagulation in patients with severe fever with thrombocytopenia syndrome","volume":"25","author":"Zhang","year":"2025","journal-title":"BMC Infect. Dis."},{"key":"10.1016\/j.isci.2026.114843_bib37","article-title":"A nomogram to predict mortality in patients with severe fever with thrombocytopenia syndrome","volume":"14","author":"Fang","year":"2024","journal-title":"Sci. Rep."},{"key":"10.1016\/j.isci.2026.114843_bib38","doi-asserted-by":"crossref","first-page":"1509","DOI":"10.1097\/CCM.0000000000006367","article-title":"The Association Between Elevated Myocardial Injury-Related Biomarker (TnI) and Increased Mortality in Patients With Severe Fever With Thrombocytopenia Syndrome","volume":"52","author":"Liang","year":"2024","journal-title":"Crit. Care Med."},{"key":"10.1016\/j.isci.2026.114843_bib39","doi-asserted-by":"crossref","first-page":"93","DOI":"10.1186\/s40249-022-01017-4","article-title":"Transmission and mortality risk assessment of severe fever with thrombocytopenia syndrome in China: results from 11-years\u2019 study","volume":"11","author":"Chen","year":"2022","journal-title":"Infect. Dis. Poverty"},{"key":"10.1016\/j.isci.2026.114843_bib40","doi-asserted-by":"crossref","first-page":"1425","DOI":"10.4269\/ajtmh.20-0270","article-title":"Clinical Progress and Risk Factors for Death from Severe Fever with Thrombocytopenia Syndrome: A Multihospital Retrospective Investigation in Anhui, China","volume":"104","author":"Gong","year":"2021","journal-title":"Am. J. Trop. Med. Hyg."},{"key":"10.1016\/j.isci.2026.114843_bib41","article-title":"Acute kidney injury as a prognostic marker in severe fever with thrombocytopenia syndrome","volume":"14","author":"Lee","year":"2024","journal-title":"Sci. Rep."},{"key":"10.1016\/j.isci.2026.114843_bib42","doi-asserted-by":"crossref","first-page":"438","DOI":"10.1038\/s41564-018-0317-1","article-title":"Ferret animal model of severe fever with thrombocytopenia syndrome phlebovirus for human lethal infection and pathogenesis","volume":"4","author":"Park","year":"2019","journal-title":"Nat. Microbiol."},{"key":"10.1016\/j.isci.2026.114843_bib43","doi-asserted-by":"crossref","first-page":"393","DOI":"10.1016\/j.jiph.2023.01.007","article-title":"Development and validation of a clinical risk score to predict the occurrence of critical illness in hospitalized patients with SFTS","volume":"16","author":"Zhang","year":"2023","journal-title":"J. Infect. Public Health"},{"key":"10.1016\/j.isci.2026.114843_bib44","doi-asserted-by":"crossref","first-page":"975","DOI":"10.1186\/s12879-024-09898-6","article-title":"Establishment and validation of a clinical risk scoring model to predict fatal risk in SFTS hospitalized patients","volume":"24","author":"Zhong","year":"2024","journal-title":"BMC Infect. Dis."},{"key":"10.1016\/j.isci.2026.114843_bib45","doi-asserted-by":"crossref","first-page":"e507","DOI":"10.1016\/S2589-7500(24)00065-7","article-title":"A prognostic model for use before elective surgery to estimate the risk of postoperative pulmonary complications (GSU-Pulmonary Score): a development and validation study in three international cohorts (2024)","volume":"6","author":"Bravo","year":"2024","journal-title":"Lancet Digit. Health"},{"key":"10.1016\/j.isci.2026.114843_bib46","doi-asserted-by":"crossref","first-page":"904","DOI":"10.1186\/s12879-021-06627-1","article-title":"Clinical feature of severe fever with thrombocytopenia syndrome (SFTS)-associated encephalitis\/encephalopathy: a retrospective study","volume":"21","author":"Xu","year":"2021","journal-title":"BMC Infect. Dis."},{"key":"10.1016\/j.isci.2026.114843_bib47","article-title":"How to develop a more accurate risk prediction model when there are few events","volume":"351","author":"Pavlou","year":"2015","journal-title":"BMJ"},{"key":"10.1016\/j.isci.2026.114843_bib48","doi-asserted-by":"crossref","first-page":"S574","DOI":"10.21037\/jtd.2019.01.25","article-title":"Developing prediction models for clinical use using logistic regression: an overview","volume":"11","author":"Shipe","year":"2019","journal-title":"J. Thorac. Dis."},{"key":"10.1016\/j.isci.2026.114843_bib49","doi-asserted-by":"crossref","first-page":"2595","DOI":"10.1093\/bioinformatics\/btv153","article-title":"PRROC: computing and visualizing precision-recall and receiver operating characteristic curves in R","volume":"31","author":"Grau","year":"2015","journal-title":"Bioinformatics"},{"key":"10.1016\/j.isci.2026.114843_bib50","doi-asserted-by":"crossref","first-page":"1377","DOI":"10.1001\/jama.2017.12126","article-title":"Discrimination and Calibration of Clinical Prediction Models: Users\u2019 Guides to the Medical Literature","volume":"318","author":"Alba","year":"2017","journal-title":"JAMA"}],"container-title":["iScience"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S258900422600218X?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S258900422600218X?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,9,9]],"date-time":"2026-09-09T10:39:12Z","timestamp":1788950352000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S258900422600218X"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,3]]},"references-count":50,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2026,3]]}},"alternative-id":["S258900422600218X"],"URL":"https:\/\/doi.org\/10.1016\/j.isci.2026.114843","relation":{},"ISSN":["2589-0042"],"issn-type":[{"value":"2589-0042","type":"print"}],"subject":[],"published":{"date-parts":[[2026,3]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Machine learning models for early mortality prediction in severe fever with thrombocytopenia syndrome","name":"articletitle","label":"Article Title"},{"value":"iScience","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.isci.2026.114843","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 The Author(s). Published by Elsevier Inc.","name":"copyright","label":"Copyright"}],"article-number":"114843"}}