Machine Learning for Prediction of Outcomes in Cardiogenic Shock.

ABSTRACT:

Objective

The management of cardiogenic shock (CS) in the elderly remains a major clinical challenge. Existing clinical prediction models have not performed well in assessing the prognosis of elderly patients with CS. This study aims to build a predictive model, which could better predict the 30-day mortality of elderly patients with CS.

Methods

We extracted data from the Medical Information Mart for Intensive Care III version 1.4 (MIMIC-III) as the training set and the data of validation sets were collected from the Second Affiliated Hospital and Yuying Children's Hospital of Wenzhou Medical University. Three models, including the cox regression model, the Least Absolute Shrinkage and Selection Operator (LASSO) regression model, and the CoxBoost model, were established usin

SUBMITTER: Rong F 

PROVIDER: S-EPMC9120613 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature