Development and validation of a reinforcement learning algorithm to dynamically optimize mechanical ventilation in critical care
Development and validation of a reinforcement learning algorithm to dynamically optimize mechanical ventilation in critical care (Nature).
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).
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In conclusion, VentAI provides reproducible high performance by dynamically choosing an optimized, individualized ventilation strategy and thus might be of benefit for critically ill patients.
Quelle: Nature, 19.02.2021
