Unifying cardiovascular modelling with deep reinforcement learning for uncertainty aware control of sepsis treatment.
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...
Main Authors: | Thesath Nanayakkara, Gilles Clermont, Christopher James Langmead, David Swigon |
---|---|
Format: | Article |
Language: | English |
Published: |
Public Library of Science (PLoS)
2022-02-01
|
Series: | PLOS Digital Health |
Online Access: | https://doi.org/10.1371/journal.pdig.0000012 |
Similar Items
-
Unifying cardiovascular modelling with deep reinforcement learning for uncertainty aware control of sepsis treatment
by: Thesath Nanayakkara, et al.
Published: (2022-02-01) -
A Three-Tiered Study of Differences in Murine Intrahost Immune Response to Multiple Pneumococcal Strains.
by: Ericka Mochan-Keef, et al.
Published: (2015-01-01) -
Unifying thermodynamic uncertainty relations
by: Gianmaria Falasco, et al.
Published: (2020-01-01) -
Towards more efficient and robust evaluation of sepsis treatment with deep reinforcement learning
by: Chao Yu, et al.
Published: (2023-03-01) -
PRGFlow: Unified SWAP‐aware deep global optical flow for aerial robot navigation
by: Nitin J. Sanket, et al.
Published: (2021-08-01)