Artificial intelligence forecasting mortality at an intensive care unit and comparison to a logistic regression system.

ABSTRACT:

Objective

To explore an artificial intelligence approach based on gradient-boosted decision trees for prediction of all-cause mortality at an intensive care unit, comparing its performance to a recent logistic regression system in the literature, and a logistic regression model built on the same platform.

Methods

A gradient-boosted decision trees model and a logistic regression model were trained and tested with the Medical Information Mart for Intensive Care database. The 1-hour resolution physiological measurements of adult patients, collected during 5 hours in the intensive care unit, consisted of eight routine clinical parameters. The study addressed how the models learn to categorize patients to predict intensive care unit mortality or survival within 12 hours. The perf

SUBMITTER: Nistal-Nuno B 

PROVIDER: S-EPMC8483638 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature