Unifying Cardiovascular Modelling with Deep Reinforcement Learning for Uncertainty Aware Control of Sepsis Treatment
arXiv:2101.08477 · doi:10.1371/journal.pdig.0000012
Abstract
Sepsis is a potentially life threatening inflammatory response to infection or severe tissue damage. It has a highly variable clinical course, requiring constant monitoring of the patient's state to guide the management of intravenous fluids and vasopressors, among other interventions. Despite decades of research, there's still debate among experts on optimal treatment. Here, we combine for the first time, distributional deep reinforcement learning with mechanistic physiological models to find personalized sepsis treatment strategies. Our method handles partial observability by leveraging known cardiovascular physiology, introducing a novel physiology-driven recurrent autoencoder, and quantifies the uncertainty of its own results. Moreover, we introduce a framework for uncertainty aware decision support with humans in the loop. We show that our method learns physiologically explainable, robust policies that are consistent with clinical knowledge. Further our method consistently identifies high risk states that lead to death, which could potentially benefit from more frequent vasopressor administration, providing valuable guidance for future research
References in corpus (8)
- Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation
- A Review of Uncertainty Quantification in Deep Learning: Techniques, Applications and Challenges
- Uncertainty-Aware Reinforcement Learning for Collision Avoidance
- Distributional Reinforcement Learning with Quantile Regression
- Deep Reinforcement Learning for Sepsis Treatment
- Improving Sepsis Treatment Strategies by Combining Deep and Kernel-Based Reinforcement Learning
- An Empirical Study of Representation Learning for Reinforcement Learning in Healthcare
- Optimizing Sequential Medical Treatments with Auto-Encoding Heuristic Search in POMDPs