Development and validation of a reinforcement learning algorithm to dynamically optimize mechanical ventilation in critical care.

ABSTRACT: The aim of this work was to develop and evaluate the reinforcement learning algorithm VentAI, which is able to suggest a dynamically optimized mechanical ventilation regime for critically-ill patients. We built, validated and tested its performance on 11,943 events of volume-controlled mechanical ventilation derived from 61,532 distinct ICU admissions and tested it on an independent, secondary dataset (200,859 ICU stays; 25,086 mechanical ventilation events). A patient "data fingerprint" of 44 features was extracted as multidimensional time series in 4-hour time steps. We used a Markov decision process, including a reward system and a Q-learning approach, to find the optimized settings for positive end-expiratory pressure (PEEP), fraction of inspired oxygen (FiO₂) and ideal body

SUBMITTER: Peine A 

PROVIDER: S-EPMC7895944 | biostudies-literature | 2021 Feb

REPOSITORIES: biostudies-literature