Constructing evidence-based treatment strategies using methods from computer science.

By Joelle Pineau, Marc G. Bellemare, A. Rush, Adrian Ghizaru, Susan A. Murphy. 2007

56 citations recorded by the source corpus.

Subfields: Experimental and Cognitive Psychology, Pharmacology, Medical & Health AI

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Citations in the corpus

Reinforcement Learning in Healthcare: A Survey (2019, methodology): “[181] first applied Kernel-based BRL [171] to construct useful DTRs for patients with MDD.”
Estimating treatment effect heterogeneity in randomized program evaluation (2013, background): “…a large number of available treatments, (2) designing optimal treatment regimes for each individual or a group of individuals [e.g., Manski (2004), Pineau et al. (2007), Moodie, Platt and Kramer (2009), Imai and Strauss (2011), Cai et al. (2011), Gunter, Zhu and Murphy (2011), Qian and Murphy…”
Informing sequential clinical decision-making through reinforcement learning: an empirical study (2010, methodology): “This problem design is similar to the problem of estimating treatment policies in the medical sciences (Murphy 2003; Pineau et al. 2007).”
Augmented outcome-weighted learning for estimating optimal dynamic treatment regimens (2018, methodology): “Following the works of Chakraborty and Moodie 14 and Pineau et al, 23 we focused on a two-stage decision-making problem by combining study levels 2 and 2A as the first stage and treating study level 3 as the second stage.”
Efficient Reinforcement Learning with Multiple Reward Functions for Randomized Controlled Trial Analysis (2010, methodology): “Reinforcement learning methods (Pineau et al., 2007) have been used in evidence-based medicine to analyze multi-stage randomized controlled trials (Murphy et al.”
Linear Fitted-Q Iteration with Multiple Reward Functions (2012, methodology): “Reinforcement learning methods (Szepesvári, 2010; Sutton and Barto, 1998) are already being used (Pineau et al., 2007; Murphy et al., 2007; Zhao et al., 2009) to analyze Sequential Multiple Assignment Randomized Trials (SMART) (Murphy, 2005).”
New Statistical Learning Methods for Estimating Optimal Dynamic Treatment Regimes (2015, methodology): “The Q-learning algorithm, originally proposed in the computer science literature, has become a powerful tool to discover optimal DTRs in the clinical research arena (Murphy et al. 2007; Pineau et al. 2007; Zhao, Kosorok, and Zeng 2009; Nahum-Shani et al. 2012).”
Control of Blood Glucose for Type-1 Diabetes by Using Reinforcement Learning with Feedforward Algorithm (2018, background): “Recently, together with the development of artificial intelligence and machine learning, reinforcement learning (RL) has emerged as a data-driven method to control unknown nonlinear systems [6, 7] and has been used as a long-termmanagement tool for chronic diseases [8, 9].”
Reinforcement learning design for cancer clinical trials (2009, unknown): “Reinforcement learning has been applied to treating behavioral disorders, where each patient typically has multiple opportunities to try different treatments [8].”
Bias Correction and Confidence Intervals for Fitted Q-iteration (2008, background): “The problem of learning a good treatment policy from a training set of finite-horizon trajectories generated by a known behavior policy often arises in medical applications [1, 2, 3, 4].”
Adaptive Interventions Treatment Modelling and Regimen Optimization Using Sequential Multiple Assignment Randomized Trials (Smart) and Q-Learning (2018, background): “In the field of health and medical studies, RL has used in treatment of behavioral disorders where patients were administered multiple treatments in different treatment stages [8].”
Regularization in reinforcement learning (2011, methodology): “Healthcare applications of reinforcement learning methods, and especially the dynamic treatment regime problem, are emerging [Pineau et al., 2007].”