Using Machine Learning to Develop and Validate an In-Hospital Mortality Prediction Model for Patients with Suspected Sepsis.

ABSTRACT:

Background

Early recognition of sepsis and the prediction of mortality in patients with infection are important. This multi-center, ED-based study aimed to develop and validate a 28-day mortality prediction model for patients with infection using various machine learning (ML) algorithms.

Methods

Patients with acute infection requiring intravenous antibiotic treatment during the first 24 h of admission were prospectively recruited. Patient demographics, comorbidities, clinical signs and symptoms, laboratory test data, selected sepsis-related novel biomarkers, and 28-day mortality were collected and divided into training (70%) and testing (30%) datasets. Logistic regression and seven ML algorithms were used to develop the prediction models. The area under the receiver operatin

SUBMITTER: Chao HY 

PROVIDER: S-EPMC9030924 | biostudies-literature | 2022 Mar

REPOSITORIES: biostudies-literature