Identification of a Susceptible and High-Risk Population for Postoperative Systemic Inflammatory Response Syndrome in Older Adults: Machine Learning-Based Predictive Model.

ABSTRACT:

Background

Systemic inflammatory response syndrome (SIRS) is a serious postoperative complication among older adult surgical patients that frequently develops into sepsis or even death. Notably, the incidences of SIRS and sepsis steadily increase with age. It is important to identify the risk of postoperative SIRS for older adult patients at a sufficiently early stage, which would allow preemptive individualized enhanced therapy to be conducted to improve the prognosis of older adult patients. In recent years, machine learning (ML) models have been deployed by researchers for many tasks, including disease prediction and risk stratification, exhibiting good application potential.

Objective

We aimed to develop and validate an individualized predictive model to identify suscept

SUBMITTER: Mai H 

PROVIDER: S-EPMC11624453 | biostudies-literature | 2024 Nov

REPOSITORIES: biostudies-literature