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Abstract This study aimed to develop and validate machine learning (ML) models for the early prediction of incident sepsis in critically ill trauma patients using the Medical Information Mart for Intensive Care IV (MIMIC-IV) database. Adult trauma patients with an intensive care unit (ICU) stay of at least 24 h were analysed, excluding those with sepsis or suspected infection during the first 24 h.
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