Development and validation of an interpretable machine learning model for retrospective identification of suspected infection for sepsis surveillance: a multicentre cohort study.

ABSTRACT:

Background

How to identify suspected infection for sepsis surveillance purposes remains a well-recognised challenge. This study aimed to operationalise suspected infection for sepsis surveillance by developing an interpretable machine learning (ML) model for retrospective identification of patients with sepsis.

Methods

This multicentre cohort and machine learning study was conducted in two Dutch tertiary care hospitals. Adult patients with a quick Sequential Organ Failure assessment (qSOFA) ≥2 were included. Exclusion criteria included admission to the intensive care unit, transfer to or from another hospital, or patient refusal to reuse data. Cohort one consisted of patients admitted to the Emergency Department (ED) of hospital A between 01/01/2019 and 12/31/2019, to invest

SUBMITTER: Tuinte RAM 

PROVIDER: S-EPMC12355416 | biostudies-literature | 2025 Sep

REPOSITORIES: biostudies-literature