Prediction of intensive care unit admission (>24h) after surgery in elective noncardiac surgical patients using machine learning algorithms.

ABSTRACT:

Background

To develop a highly discriminative machine learning model for the prediction of intensive care unit admission (>24h) using the easily available preoperative information from electronic health records. An accurate prediction model for ICU admission after surgery is of great importance for surgical risk assessment and appropriate utilization of ICU resources.

Method

Data were collected retrospectively from a large hospital, comprising 135,442 adult patients who underwent surgery except for cardiac surgery between 1 January 2014, and 31 July 2018 in China. Multiple existing predictive machine learning algorithms were explored to construct the prediction model, including logistic regression, random forest, adaptive boosting, and gradient boosting machine. Four seconda

SUBMITTER: Lan L 

PROVIDER: S-EPMC9326842 | biostudies-literature | 2022 Jan-Dec

REPOSITORIES: biostudies-literature