Deep Reinforcement Learning and Simulation as a Path Toward Precision Medicine
- Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)
- Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States); Georgia Inst. of Technology, Atlanta, GA (United States)
- Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States); Univ. of Toronto, ON (Canada)
- Univ. of Vermont, Burlington, VT (United States); Univ. of Chicago, IL (United States)
In the past, precision medicine involves classifying patients to identify subpopulations that respond favorably to specific therapeutics. We discuss 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 believe that this work motivates both considering adaptive personalized multicytokine mediation therapy for sepsis and exploiting simulation with DRL for precision medicine more broadly.
- Research Organization:
- Lawrence Livermore National Laboratory (LLNL), Livermore, CA (United States)
- Sponsoring Organization:
- USDOE National Nuclear Security Administration (NNSA); National Institutes of Health (NIH)
- Grant/Contract Number:
- AC52-07NA27344
- OSTI ID:
- 1477825
- Report Number(s):
- LLNL-JRNL-745693; 900608
- Journal Information:
- Journal of Computational Biology, Vol. 26, Issue 6; ISSN 1557-8666
- Country of Publication:
- United States
- Language:
- English
Web of Science
Agent‐based models of inflammation in translational systems biology: A decade later
|
journal | June 2019 |
| Supporting Computational Apprenticeship Through Educational and Software Infrastructure: A Case Study in a Mathematical Oncology Research Lab | journal | February 2021 |
Similar Records
Examining the controllability of sepsis using genetic algorithms on an agent-based model of systemic inflammation
Sepsis reconsidered: Identifying novel metrics for behavioral landscape characterization with a high-performance computing implementation of an agent-based model