Patient-Specific Sedation Management via Deep Reinforcement Learning

• Main Authors: Niloufar Eghbali (Author), Tuka Alhanai (Author), Mohammad M. Ghassemi (Author)
• Format: Book
• Published: Frontiers Media S.A., 2021-03-01T00:00:00Z.
• Subjects: article
• Online Access: Connect to this object online.
• Notes: 2673-253X; 10.3389/fdgth.2021.608893
• Keywords: medication dosing; personalized medicine; deep reinforcement learning; propofol; sedation management; Medicine; R; Public aspects of medicine; RA1-1270; Electronic computers. Computer science; QA75.5-76.95
• Source: Frontiers in Digital Health, Vol 3 (2021)

Introduction: Developing reliable medication dosing guidelines is challenging because individual dose-response relationships are mitigated by both static (e. g., demographic) and dynamic factors (e.g., kidney function). In recent years, several data-driven medication dosing models have been proposed for sedatives, but these approaches have been limited in their ability to assess interindividual differences and compute individualized doses.Objective: The primary objective of this study is to develop an individualized framework for sedative-hypnotics dosing.Method: Using publicly available data (1,757 patients) from the MIMIC IV intensive care unit database, we developed a sedation management agent using deep reinforcement learning. More specifically, we modeled the sedative dosing problem as a Markov Decision Process and developed an RL agent based on a deep deterministic policy gradient approach with a prioritized experience replay buffer to find the optimal policy. We assessed our method's ability to jointly learn an optimal personalized policy for propofol and fentanyl, which are among commonly prescribed sedative-hypnotics for intensive care unit sedation. We compared our model's medication performance against the recorded behavior of clinicians on unseen data.Results: Experimental results demonstrate that our proposed model would assist clinicians in making the right decision based on patients' evolving clinical phenotype. The RL agent was 8% better at managing sedation and 26% better at managing mean arterial compared to the clinicians' policy; a two-sample t-test validated that these performance improvements were statistically significant (p < 0.05).Conclusion: The results validate that our model had better performance in maintaining control variables within their target range, thereby jointly maintaining patients' health conditions and managing their sedation.