Machine learning algorithms for risk factor selection with application to 60-day sepsis morbidity risk for a geriatric hip fracture cohort.

ABSTRACT:

Background

Sepsis after hip fracture in elderly people is a risk factor for mortality. The purpose of this study was to screen for risk factors for 60-day sepsis morbidity after hip fracture and to establish a predictive model using various machine learning algorithms.

Methods

A total of 697 patients who were older than 65 years of age were selected; 590 patients were divided into training and test sets (at a ratio of 7:3), and 107 individuals were used for external validation. Least absolute shrinkage and selection operator (LASSO), the Boruta algorithm, and random survival forest (RSF) were used to screen for risk factors for sepsis incidence. Correlation coefficients and variance inflation factors (VIFs) were calculated to assess multicollinearity. Cox proportional hazard

SUBMITTER: Xu Z 

PROVIDER: S-EPMC12323276 | biostudies-literature | 2025 Aug

REPOSITORIES: biostudies-literature