Accepted for/Published in: JMIR Formative Research
Date Submitted: Feb 17, 2021
Date Accepted: Aug 1, 2021
Date Submitted to PubMed: Aug 16, 2021
Warning: This is an author submission that is not peer-reviewed or edited. Preprints - unless they show as "accepted" - should not be relied on to guide clinical practice or health-related behavior and should not be reported in news media as established information.
Accurate ARDS prediction from standard time series clinical data using gradient boosted trees and semi supervised deep learning
ABSTRACT
Background:
A high number of patients hospitalized with COVID-19 also develop Acute Respiratory Distress Syndrome (ARDS).
Objective:
In response to the need for clinical decision support tools to help manage the pandemic, we developed a semi-supervised machine learning (SSL) algorithm to predict ARDS in general and COVID-19 populations.
Methods:
SSL techniques were applied to 53,432 encounters from patients admitted to the intensive care unit at a large academic health center between 2001-2012. SSL techniques were also applied to 45,507 COVID-19 encounters from patients admitted to ten United States hospitals from 2019-2020. A baseline XGBoost (XGB) model and an evolving recurrent neural network (RNN) model trained using SSL were applied to the task of predicting ARDS at 12-, 24-, and 48- hours prior to onset. Model performance was assessed with regard to area under the receiver operating characteristic (AUROC).
Results:
Higher performance was achieved by the SSL as compared to the baseline XGB model in the general ICU population and much higher performance was achieved by the SSL in the COVID-19 positive population. The SSL demonstrated AUROCs of 0.96 for all time frames while baseline XGB demonstrated AUROCs of 0.75 in the same time frames. A cluster analysis demonstrated that ARDS in COVID-19 populations presented dissimilarly from ARDS in general populations.
Conclusions:
An SSL model outperformed an XGB model for early ARDS prediction in both general and COVID-19 patient populations. This study supports that ARDS in COVID-19 patients warrants further research as a distinct clinical event. Clinical Trial: N/A
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