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Reinforcement Learning Using Neural Networks in Estimating an Optimal Dynamic Treatment Regime in Patients with Sepsis: A Retrospective Cohort Study Based on the Mimic-Iii Database

https://doi.org/10.2139/ssrn.4744623
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Q‐learning for estimating optimal dynamic treatment rules from observational data
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Reinforcement learning design for cancer clinical trials
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Optimization of multi‐stage dynamic treatment regimes utilizing accumulated data
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Estimating optimal treatment regimes from a classification perspective
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Optimal Structural Nested Models for Optimal Sequential Decisions
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Approximation by superpositions of a sigmoidal function
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Surviving Sepsis Campaign: International Guidelines for Management of Sepsis and Septic Shock: 2016
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Expert statement for the management of hypovolemia in sepsis
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Sepsis hospitalization and risk of subsequent cardiovascular events in adults: a population-based matched cohort study
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Approximation capabilities of multilayer feedforward networks
inferredcites →https://doi.org/10.1038/sdata.2016.35openalex-extracted
MIMIC-III, a freely accessible critical care database
inferredcites →https://doi.org/10.1056/nejmra1208623openalex-extracted
Severe Sepsis and Septic Shock
inferredcites →https://doi.org/10.1080/01621459.2011.641416openalex-extracted
Evaluation of Viable Dynamic Treatment Regimes in a Sequentially Randomized Trial of Advanced Prostate Cancer
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Estimating Individualized Treatment Rules Using Outcome Weighted Learning
inferredcites →https://doi.org/10.1080/01621459.2014.937488openalex-extracted
New Statistical Learning Methods for Estimating Optimal Dynamic Treatment Regimes
inferredcites →https://doi.org/10.1080/01621459.2015.1093947openalex-extracted
Residual Weighted Learning for Estimating Individualized Treatment Rules
inferredcites →https://doi.org/10.1080/01621459.2017.1340887openalex-extracted
A Bayesian Machine Learning Approach for Optimizing Dynamic Treatment Regimes
inferredcites →https://doi.org/10.1080/01621459.2020.1753522openalex-extracted
Robust Q-Learning
inferredcites →https://doi.org/10.1080/01621459.2020.1819294openalex-extracted
Stochastic Tree Search for Estimating Optimal Dynamic Treatment Regimes
inferredcites →https://doi.org/10.1090/s0002-9904-1954-09848-8openalex-extracted
The theory of dynamic programming
inferredcites →https://doi.org/10.1093/biomet/ast014openalex-extracted
Robust estimation of optimal dynamic treatment regimes for sequential treatment decisions
inferredcites →https://doi.org/10.1093/biomet/asy043openalex-extracted
Constructing dynamic treatment regimes over indefinite time horizons
inferredcites →https://doi.org/10.1097/00003246-200107000-00002openalex-extracted
Epidemiology of severe sepsis in the United States: Analysis of incidence, outcome, and associated costs of care
inferredcites →https://doi.org/10.1109/access.2022.3193494openalex-extracted
Learning and Assessing Optimal Dynamic Treatment Regimes Through Cooperative Imitation Learning
inferredcites →https://doi.org/10.1111/1467-9868.00389openalex-extracted
Optimal Dynamic Treatment Regimes
inferredcites →https://doi.org/10.1111/biom.12052openalex-extracted
Inference for Optimal Dynamic Treatment Regimes Using an Adaptive m‐Out‐of‐n Bootstrap Scheme
inferredcites →https://doi.org/10.1111/biom.12539openalex-extracted
Adaptive Contrast Weighted Learning for Multi-Stage Multi-Treatment Decision-Making
inferredcites →https://doi.org/10.1111/biom.12836openalex-extracted
C-learning: A new Classification Framework to Estimate Optimal Dynamic Treatment Regimes
inferredcites →https://doi.org/10.1111/j.1541-0420.2011.01572.xopenalex-extracted
Reinforcement Learning Strategies for Clinical Trials in Nonsmall Cell Lung Cancer
inferredcites →https://doi.org/10.1111/j.1541-0420.2012.01763.xopenalex-extracted
A Robust Method for Estimating Optimal Treatment Regimes
inferredcites →https://doi.org/10.1137/s0363012901385691openalex-extracted
OnActor-Critic Algorithms
inferredcites →https://doi.org/10.1164/rccm.202112-2743ocopenalex-extracted
Epidemiology, Management, and Outcomes of Sepsis in ICUs among Countries of Differing National Wealth across Asia
inferredcites →https://doi.org/10.1177/09622802221122397openalex-extracted
Penalized Spline-Involved Tree-based (PenSIT) Learning for estimating an optimal dynamic treatment regime using observational data
inferredcites →https://doi.org/10.1186/s12911-019-0755-6openalex-extracted
Incorporating causal factors into reinforcement learning for dynamic treatment regimes in HIV
inferredcites →https://doi.org/10.1198/016214501753382327openalex-extracted
Marginal Mean Models for Dynamic Regimes
inferredcites →https://doi.org/10.1214/13-sts450openalex-extracted
$\mathbf{Q}$- and $\mathbf{A}$-Learning Methods for Estimating Optimal Dynamic Treatment Regimes
inferredcites →https://doi.org/10.1214/14-ejs920openalex-extracted
Dynamic treatment regimes: Technical challenges and applications
inferredcites →https://doi.org/10.1214/18-aoas1137openalex-extracted
Tree-based reinforcement learning for estimating optimal dynamic treatment regimes
inferredcites →https://doi.org/10.1287/mnsc.2022.00883openalex-extracted
Ambiguous Dynamic Treatment Regimes: A Reinforcement Learning Approach
inferredcites →https://doi.org/10.1377/hlthaff.20.6.64openalex-extracted
Improving Chronic Illness Care: Translating Evidence Into Action
inferredcites →https://doi.org/10.2139/ssrn.3980837openalex-extracted
Ambiguous Dynamic Treatment Regimes: A Reinforcement Learning Approach
inferredcites →https://doi.org/10.2202/1557-4679.1200openalex-extracted
Dynamic Regime Marginal Structural Mean Models for Estimation of Optimal Dynamic Treatment Regimes, Part I: Main Content
inferredcites →https://doi.org/10.4161/viru.27372openalex-extracted
Epidemiology of severe sepsis
inferredcites →https://doi.org/10.5705/ss.2012.364openalex-extracted
Penalized Q-learning for dynamic treatment regimens
inferredcites →https://doi.org/10.5705/ss.202016.0133openalex-extracted
Sensitivity Analysis for Unmeasured Confounding in Coarse Structural Nested Mean Models
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