Deep Reinforcement Learning and Simulation as a Path Toward Precision Medicine

Journal of computational biology: A journal of computational molecular cell biology

Will S.Grathwohl^1¹  Brenden K.Petersen^1²  JiachenYang^1³ 

[1] Address correspondence to: Dr. Daniel M. Faissol, Computational Engineering Division, Lawrence Livermore National Laboratory, 7000 East Ave., Livermore, CA 94550^3;Computational Engineering Division, Lawrence Livermore National Laboratory, Livermore, California^1;Department of Surgery, University of Vermont, Burlington, Vermont^2

关键词: agent-based model;    deep reinforcement learning;    precision medicine;    sepsis.;   

DOI  :  10.1089/cmb.2018.0168

学科分类：生物科学（综合）

来源: Mary Ann Liebert, Inc. Publishers

【 摘 要 】

Traditionally, precision medicine involves classifying patients to identify subpopulations that respond favorably to specific therapeutics. We pose precision medicine as a dynamic feedback control problem, where treatment administered to a patient is guided by measurements taken during the course of treatment. We consider sepsis, a life-threatening condition in which dysregulation of the immune system causes tissue damage. We leverage an existing simulation of the innate immune response to infection and apply deep reinforcement learning (DRL) to discover an adaptive personalized treatment policy that specifies effective multicytokine therapy to simulated sepsis patients based on systemic measurements. The learned policy achieves a dramatic reduction in mortality rate over a set of 500 simulated patients relative to standalone antibiotic therapy. Advantages of our approach are threefold: (1) the use of simulation allows exploring therapeutic strategies beyond clinical practice and available data, (2) advances in DRL accommodate learning complex therapeutic strategies for complex biological systems, and (3) optimized treatments respond to a patient's individual disease progression over time, therefore, capturing both differences across patients and the inherent randomness of disease progression within a single patient. We hope that this work motivates both considering adaptive personalized multicytokine mediation therapy for sepsis and exploiting simulation with DRL for precision medicine more broadly.

【 授权许可】

Unknown