A Bayesian Machine Learning Approach for Optimizing Dynamic Treatment Regimes

DOI10.1080/01621459.2017.1340887zbMath1402.62281OpenAlexW2691614902WikidataQ91441350 ScholiaQ91441350MaRDI QIDQ4559706

Ying Yuan, Thomas A. Murray, Peter F. Thall

Publication date: 4 December 2018

Published in: Journal of the American Statistical Association (Search for Journal in Brave)

Full work available at URL: http://europepmc.org/articles/pmc6366650

zbMATH Keywords

Gibbs samplingbackward inductionpotential outcomesapproximate dynamic programmingBayesian additive regression trees

Mathematics Subject Classification ID

Applications of statistics to biology and medical sciences; meta analysis (62P10) Learning and adaptive systems in artificial intelligence (68T05)

Related Items

Data-guided Treatment Recommendation with Feature Scores, Covariate-modulated large-scale multiple testing under dependence, Designing optimal, data-driven policies from multisite randomized trials, Dynamic treatment regimes using Bayesian additive regression trees for censored outcomes, Stochastic Tree Search for Estimating Optimal Dynamic Treatment Regimes

Uses Software

• qLearn

Cites Work

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