{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"omics_type":["Unknown"],"volume":["7(12)"],"submitter":["Abbas GH"],"pubmed_abstract":["<h4>Objectives</h4>This systematic review evaluates artificial intelligence (AI)-based predictive models developed for early sepsis detection in adult hospitalized patients. It explores model types, input features, validation strategies, performance metrics, clinical integration, and implementation challenges.<h4>Data sources</h4>A systematic search was conducted across PubMed, Scopus, Web of Science, Google Scholar, and CENTRAL for studies published between January 2015 and March 2025.<h4>Study selection</h4>Eligible studies included those developing or validating AI models for adult inpatient sepsis prediction using electronic health record data and reporting at least one performance metric (area under the curve [AUC], sensitivity, specificity, or F1 score). Studies focusing on pediatric"],"journal":["Critical care explorations"],"pagination":["e1360"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC12685403"],"repository":["biostudies-literature"],"pubmed_title":["Artificial Intelligence-Based Predictive Modeling for Early Detection of Sepsis in Hospitalized Patients: A Systematic Review and Meta-Analysis."],"pmcid":["PMC12685403"],"pubmed_authors":["Giri OA","Khan NH","Abbas GH","Sen P"],"additional_accession":[]},"is_claimable":false,"name":"Artificial Intelligence-Based Predictive Modeling for Early Detection of Sepsis in Hospitalized Patients: A Systematic Review and Meta-Analysis.","description":"<h4>Objectives</h4>This systematic review evaluates artificial intelligence (AI)-based predictive models developed for early sepsis detection in adult hospitalized patients. It explores model types, input features, validation strategies, performance metrics, clinical integration, and implementation challenges.<h4>Data sources</h4>A systematic search was conducted across PubMed, Scopus, Web of Science, Google Scholar, and CENTRAL for studies published between January 2015 and March 2025.<h4>Study selection</h4>Eligible studies included those developing or validating AI models for adult inpatient sepsis prediction using electronic health record data and reporting at least one performance metric (area under the curve [AUC], sensitivity, specificity, or F1 score). Studies focusing on pediatric","dates":{"release":"2025-01-01T00:00:00Z","publication":"2025 Dec","modification":"2026-06-07T04:52:01.822Z","creation":"2026-06-07T03:05:54.264Z"},"accession":"S-EPMC12685403","cross_references":{"pubmed":["41348160"],"doi":["10.1097/CCE.0000000000001360"]}}