ArticleJournal of machine learning research : JMLR2012
Linear Fitted-Q Iteration with Multiple Reward Functions.
Article in Journal of machine learning research : JMLR, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
19 citing papers in PubMed, 71 citations in OpenAlex.
- A Bayesian multivariate hierarchical model for developing a treatment benefit index using mixed types of outcomes.BMC medical research methodology · 2024Article
- Multiobjective tree-based reinforcement learning for estimating tolerant dynamic treatment regimes.Biometrics · 2024Article
- Improving Individualized Treatment Decisions: A Bayesian Multivariate Hierarchical Model for Developing a Treatment Benefit Index using Mixed Types of Outcomes.medRxiv : the preprint server for health sciences · 2024Article
- Optimal Personalized Treatment Selection with Multivariate Outcome Measures in a Multiple Treatment Case.Communications in statistics: Simulation and computation · 2023Article
- When the Ends do not Justify the Means: Learning Who is Predicted to Have Harmful Indirect Effects.Journal of the Royal Statistical Society. Series A, (Statistics in Society) · 2022Article
- Quantiles based personalized treatment selection for multivariate outcomes and multiple treatments.Statistics in medicine · 2022Article
- Risk controlled decision trees and random forests for precision Medicine.Statistics in medicine · 2022Article
- Multi-Response Based Personalized Treatment Selection with Data from Crossover Designs for Multiple Treatments.Communications in statistics: Simulation and computation · 2022Article
- Step-adjusted tree-based reinforcement learning for evaluating nested dynamic treatment regimes using test-and-treat observational data.Statistics in medicine · 2021Article
- Estimation and Optimization of Composite Outcomes.Journal of machine learning research : JMLR · 2021Article
- Precision Medicine.Annual review of statistics and its application · 2019Article
- Incorporating Patient Preferences into Estimation of Optimal Individualized Treatment Rules.Biometrics · 2018Article
- Learning Optimal Personalized Treatment Rules in Consideration of Benefit and Risk: with an Application to Treating Type 2 Diabetes Patients with Insulin Therapies.Journal of the American Statistical Association · 2018Article
- Multi-Objective Markov Decision Processes for Data-Driven Decision Support.Journal of machine learning research : JMLR · 2016Article
- Interactive model building forBiometrika · 2014Article
- Estimation of optimal dynamic treatment regimes.Clinical trials (London, England) · 2014Article
- Set-valued dynamic treatment regimes for competing outcomes.Biometrics · 2014Article
- Dynamic Treatment Regimes.Annual review of statistics and its application · 2014Article
- Adaptive Interventions in Child and Adolescent Mental Health.Journal of clinical child and adolescent psychology : the official journal for the Society of Clinical Child and Adolescent Psychology, American Psychological Association, Division 53Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors at 1 institution in 1 country.
Funding
Abstract
We present a general and detailed development of an algorithm for finite-horizon fitted-Q iteration with an arbitrary number of reward signals and linear value function approximation using an arbitrary number of state features. This includes a detailed treatment of the 3-reward function case using triangulation primitives from computational geometry and a method for identifying globally dominated actions. We also present an example of how our methods can be used to construct a real-world decision aid by considering symptom reduction, weight gain, and quality of life in sequential treatments for schizophrenia. Finally, we discuss future directions in which to take this work that will further enable our methods to make a positive impact on the field of evidence-based clinical decision support.
Indexed as
Identifiers
23741197PMC3670261W2167062306What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.