Triage and monitoring of COVID-19 patients in intensive care using unsupervised machine learning.

ABSTRACT:

Background

We designed an algorithm to assess COVID-19 patients severity and dynamic intubation needs and predict their length of stay using the breathing frequency (BF) and oxygen saturation (SpO₂) signals.

Methods

We recorded the BF and SpO₂ signals for confirmed COVID-19 patients admitted to the ICU of a teaching hospital during both the first and subsequent outbreaks of the pandemic in France. An unsupervised machine-learning algorithm (the Gaussian mixture model) was applied to the patients' data for clustering. The algorithm's robustness was ensured by comparing its results against actual intubation rates. We predicted intubation rates using the algorithm every hour, thus conducting a severity evaluation. We designed a S₂₄ severity scor

SUBMITTER: Boussen S 

PROVIDER: S-EPMC8719000 | biostudies-literature | 2022 Mar

REPOSITORIES: biostudies-literature