Previously submitted to: Journal of Medical Internet Research (no longer under consideration since Sep 03, 2024)
Date Submitted: Sep 15, 2023
Open Peer Review Period: Sep 15, 2023 - Nov 10, 2023
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The Artificial Intelligence Clinician learns mechanical ventilation in patients with moderate-severe acute respiratory distress syndrome: A multicenter observational study
ABSTRACT
Background:
A lung-protective ventilation strategy may require personalization to improve the outcomes of moderate to severe forms of acute respiratory distress syndrome (ARDS). Personalized medicine is inherently challenging because of heterogeneous lung stress/strain.
Objective:
We aimed to design three reward frameworks to suggest a dynamic ventilation regimen for ARDS patients.
Methods:
Artificial intelligence (AI) systems based on prognosis, platform pressure, and driving pressure were proposed that use reinforcement learning to optimize ideal body weight-adjusted tidal volume (Vt) and respiratory rate (RR). This study was a retrospective analysis of datasets from the MIMIC and eICU databases.
Results:
The study included 16,487 moderate-severe ARDS patients. We demonstrated that the reward of the prognosis-AI, platform-AI, and driving-AI clinician's selected treatment is on average reliably higher than that of human clinicians. Furthermore, mortality was lowest in ARDS patients for whom clinicians' actual doses matched the platform-AI decisions.
Conclusions:
Our model provides individualized and clinically interpretable treatment decisions for moderate-severe ARDS that could improve patient prognosis. The reward framework based on platform pressure and driving pressure might be a feasible candidate for future research on reinforcement learning models of mechanical ventilation.
Citation
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