Harness machine learning for multiple prognoses prediction in sepsis patients: evidence from the MIMIC-IV database.

ABSTRACT:

Background

Sepsis, a severe systemic response to infection, frequently results in adverse outcomes, underscoring the urgency for prompt and accurate prognostic tools. Machine learning methods such as logistic regression, random forests, and CatBoost, have shown potential in early sepsis prediction. The study aimed to create and verify a machine learning model capable of early prognostic identification of patients with sepsis in intensive care units (ICUs).

Methods

Patients adhering to inclusion and exclusion criteria from the MIMIC-IV v2.2 database were divided into a training set and a validation set in a 7:3 ratio. Initially, we employed difference analysis to assess the significance of each variable and subsequently screened relevant features with multinomial logistic reg

SUBMITTER: Zhang SZ 

PROVIDER: S-EPMC11959728 | biostudies-literature | 2025 Mar

REPOSITORIES: biostudies-literature