Reinforcement learning strategies for clinical trials in nonsmall cell lung cancer
about
Q-learning for estimating optimal dynamic treatment rules from observational data.Dynamic Treatment RegimesInference about the expected performance of a data-driven dynamic treatment regime.Doubly Robust Learning for Estimating Individualized Treatment with Censored DataOptimization of multi-stage dynamic treatment regimes utilizing accumulated dataOptimization of individualized dynamic treatment regimes for recurrent diseases.Doubly Robust Estimation of Optimal Dynamic Treatment Regimes.Estimation of optimal dynamic treatment regimesDiscussion of "Combining biomarkers to optimize patient treatment recommendation".Interactive model building for Q-learning.SMART designs in cancer research: Past, present, and future.Using decision lists to construct interpretable and parsimonious treatment regimes.Tree-based methods for individualized treatment regimes.Estimating Individualized Treatment Rules Using Outcome Weighted LearningA decision-theoretic phase I-II design for ordinal outcomes in two cyclesSequential advantage selection for optimal treatment regimeRecent development on statistical methods for personalized medicine discovery.Personalizing medicine: a review of adaptive treatment strategies.Identifying the miRNA signature associated with survival time in patients with lung adenocarcinoma using miRNA expression profiles.Tuberculosis disease diagnosis using artificial immune recognition system.Incorporating Patient Preferences into Estimation of Optimal Individualized Treatment Rules.Dynamic treatment regimes, past, present, and future: A conversation with experts.Innovative Clinical Trial Designs for Precision Medicine in Heart Failure with Preserved Ejection Fraction.On Sparse representation for Optimal Individualized Treatment Selection with Penalized Outcome Weighted Learning.Estimating restricted mean treatment effects with stacked survival models.Bayesian Dose-Finding in Two Treatment Cycles Based on the Joint Utility of Efficacy and Toxicity.Penalized Q-Learning for Dynamic Treatment RegimensNew Statistical Learning Methods for Estimating Optimal Dynamic Treatment Regimes.ESTIMATING MEAN SURVIVAL TIME: WHEN IS IT POSSIBLE?Doubly-robust dynamic treatment regimen estimation via weighted least squares.Recursively Imputed Survival Trees.A framework for estimating and testing qualitative interactions with applications to predictive biomarkers.Semiparametric Single-Index Model for Estimating Optimal Individualized Treatment Strategy.Residual Weighted Learning for Estimating Individualized Treatment Rules.Concordance-Assisted Learning for Estimating Optimal Individualized Treatment Regimes.Decision making and uncertainty quantification for individualized treatments using Bayesian Additive Regression Trees.Tree based weighted learning for estimating individualized treatment rules with censored data.Combining parametric, semi-parametric, and non-parametric survival models with stacked survival models.Mining Major Transitions of Chronic Conditions in Patients with Multiple Chronic Conditions.LIBERTI: A SMART study in plastic surgery.
P2860
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P2860
Reinforcement learning strategies for clinical trials in nonsmall cell lung cancer
description
2011 nî lūn-bûn
@nan
2011 թուականի Մարտին հրատարակուած գիտական յօդուած
@hyw
2011 թվականի մարտին հրատարակված գիտական հոդված
@hy
2011年の論文
@ja
2011年論文
@yue
2011年論文
@zh-hant
2011年論文
@zh-hk
2011年論文
@zh-mo
2011年論文
@zh-tw
2011年论文
@wuu
name
Reinforcement learning strategies for clinical trials in nonsmall cell lung cancer
@ast
Reinforcement learning strategies for clinical trials in nonsmall cell lung cancer
@en
type
label
Reinforcement learning strategies for clinical trials in nonsmall cell lung cancer
@ast
Reinforcement learning strategies for clinical trials in nonsmall cell lung cancer
@en
prefLabel
Reinforcement learning strategies for clinical trials in nonsmall cell lung cancer
@ast
Reinforcement learning strategies for clinical trials in nonsmall cell lung cancer
@en
P2093
P2860
P1433
P1476
Reinforcement learning strategies for clinical trials in nonsmall cell lung cancer
@en
P2093
Donglin Zeng
Mark A Socinski
Michael R Kosorok
Yufan Zhao
P2860
P304
P356
10.1111/J.1541-0420.2011.01572.X
P407
P577
2011-03-08T00:00:00Z