Q-learning for estimating optimal dynamic treatment rules from observational data.
about
Dynamic Treatment RegimesInference about the expected performance of a data-driven dynamic treatment regime.Tools for the Precision Medicine Era: How to Develop Highly Personalized Treatment Recommendations From Cohort and Registry Data Using Q-Learning.Using decision lists to construct interpretable and parsimonious treatment regimes.iqLearn: Interactive Q-Learning in R.Incorporating Patient Preferences into Estimation of Optimal Individualized Treatment Rules.Statistical Reasoning and Methods in Epidemiology to Promote Individualized Health: In Celebration of the 100th Anniversary of the Johns Hopkins Bloomberg School of Public Health.Personalized Dose Finding Using Outcome Weighted Learning.Adaptive contrast weighted learning for multi-stage multi-treatment decision-making.Program for lung cancer screening and tobacco cessation: Study protocol of a sequential, multiple assignment, randomized trial.Developing adaptive interventions for adolescent substance use treatment settings: protocol of an observational, mixed-methods project.Change-point detection for infinite horizon dynamic treatment regimes.
P2860
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P2860
Q-learning for estimating optimal dynamic treatment rules from observational data.
description
2012 nî lūn-bûn
@nan
2012 թուականի Նոյեմբերին հրատարակուած գիտական յօդուած
@hyw
2012 թվականի նոյեմբերին հրատարակված գիտական հոդված
@hy
2012年の論文
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2012年学术文章
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2012年学术文章
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2012年学术文章
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2012年学术文章
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2012年学术文章
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2012年學術文章
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name
Q-learning for estimating optimal dynamic treatment rules from observational data.
@ast
Q-learning for estimating optimal dynamic treatment rules from observational data.
@en
type
label
Q-learning for estimating optimal dynamic treatment rules from observational data.
@ast
Q-learning for estimating optimal dynamic treatment rules from observational data.
@en
prefLabel
Q-learning for estimating optimal dynamic treatment rules from observational data.
@ast
Q-learning for estimating optimal dynamic treatment rules from observational data.
@en
P2093
P2860
P356
P1476
Q-learning for estimating optimal dynamic treatment rules from observational data.
@en
P2093
Bibhas Chakraborty
Erica E M Moodie
Michael S Kramer
P2860
P304
P356
10.1002/CJS.11162
P577
2012-11-07T00:00:00Z