Machine Learning Model for Sepsis Prediction in Prolonged and Chronic Critical Illness: Development and Validation Using Retrospective Real-World ICU Data.

ABSTRACT: Background: No machine learning (ML) models for sepsis prediction have been specifically developed for patients with prolonged or chronic critical illness (PCI/CCI). Objective: This study aimed to develop and validate an ML-based sepsis prediction model for this cohort. Methods: We analyzed ICU admissions from the Russian Intensive Care Dataset (RICD, 575 patients with PCI/CCI) and two public ICU datasets from the PhysioNet (>40,000 patients with acute critical illness). Models were trained within a right-aligned prediction framework using a case-crossover-control sampling approach and a 6 h prediction window. Two strategies were evaluated: (1) a PCI/CCI-focused model trained on RICD with external testing on PhysioNet data and (2) a universal model trained on combined

SUBMITTER: Yadgarov MY 

PROVIDER: S-EPMC12841784 | biostudies-literature | 2026 Jan

REPOSITORIES: biostudies-literature