Machine learning for patient risk stratification for acute respiratory distress syndrome.

ABSTRACT:

Background

Existing prediction models for acute respiratory distress syndrome (ARDS) require manual chart abstraction and have only fair performance-limiting their suitability for driving clinical interventions. We sought to develop a machine learning approach for the prediction of ARDS that (a) leverages electronic health record (EHR) data, (b) is fully automated, and (c) can be applied at clinically relevant time points throughout a patient's stay.

Methods and findings

We trained a risk stratification model for ARDS using a cohort of 1,621 patients with moderate hypoxia from a single center in 2016, of which 51 patients developed ARDS. We tested the model in a temporally distinct cohort of 1,122 patients from 2017, of which 27 patients developed ARDS. Gold standard diagnos

SUBMITTER: Zeiberg D 

PROVIDER: S-EPMC6438573 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature