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

« Einnahmen baden-württembergischer Krankenhäuser im Januar 2021 wegen Corona um rund 177 Mio. Euro gesunken | Development and validation of a reinforcement learning algorithm to dynamically optimize mechanical ventilation in critical care | Versäumnisse bei Impf-Priorisierung in der Salus Altmark Holding »

 

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...

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).
[...]

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

- - - - - - - - -



erschienen am Montag, 22.02.2021