Mortality prediction for patients with acute respiratory distress syndrome based on machine learning: a population-based study.

ABSTRACT:

Background

Traditional scoring systems for patients' outcome prediction in intensive care units such as Oxygenation Saturation Index (OSI) and Oxygenation Index (OI) may not reliably predict the clinical prognosis of patients with acute respiratory distress syndrome (ARDS). Thus, none of them have been widely accepted for mortality prediction in ARDS. This study aimed to develop and validate a mortality prediction method for patients with ARDS based on machine learning using the Medical Information Mart for Intensive Care (MIMIC-III) and Telehealth Intensive Care Unit (eICU) Collaborative Research Database (eICU-CRD) databases.

Methods

Patients with ARDS were selected based on the Berlin definition in MIMIC-III and eICU-CRD databases. The APPS score (using age, PaO₂

SUBMITTER: Huang B 

PROVIDER: S-EPMC8246239 | biostudies-literature | 2021 May

REPOSITORIES: biostudies-literature