Vital Signs-Only Machine Learning Model for Acute Inpatient Deterioration: A Retrospective Multicenter Study.

ABSTRACT:

Objective

To develop predictive models that are compatible with vital signs monitoring devices to identify patients at risk of clinical deterioration, defined as requiring a rapid response team intervention or an unplanned intensive care unit transfer.

Patients and methods

Targeted vital signs from 227,858 inpatients admitted to general care or telemetry beds at a multihospital health care institution between January 1, 2019, and July 31, 2023, were selected. After filtering for high-quality data, 30,118 patients were used to train a Light Gradient Boosting Machine, and 30,095 were reserved for blind validation. We developed a machine learning model designed to minimize false positives while maintaining clinical relevance in identifying low-prevalence clinical deterioration

SUBMITTER: Romero-Brufau S 

PROVIDER: S-EPMC12482306 | biostudies-literature | 2025 Oct

REPOSITORIES: biostudies-literature