Dynamic treatment regimes.

By Erica E. Moodie. 2004

174 citations recorded by the source corpus.

Subfields: Endocrinology, Diabetes and Metabolism, Statistics and Probability

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In Endocrinology, Diabetes and Metabolism

In Statistics and Probability

Citations in the corpus

Evaluating Reinforcement Learning Algorithms in Observational Health Settings (2018, background): “We note that identifying confounding factors is a substantial task even for the simpler problem of making a single treatment decision, and this problem is only aggravated in the sequential setting [Chakraborty and Murphy, 2014].”
A Study on Overfitting in Deep Reinforcement Learning (2018, methodology): “Meanwhile, as RL techniques being applied to important fields concerning our everyday life such as healthcare (Chakraborty & Murphy, 2014; Kosorok & Moodie, 2015) and finance (Li et al., 2009; Yu et al., 2009; Deng et al., 2017), and fundamental infrastructures such as power grids (Wen et al.,…”
Dynamic treatment regimes: technical challenges and applications (2014, methodology): “Other indirect methods include Q-learning (Murphy, 2005b; Chakraborty and Moodie, 2013; Qian et al., 2013; Chakraborty and Murphy, 2014; Laber et al., 2014), A-learning (Murphy, 2003; Robins, 2004), and regret-regression (Henderson et al., 2009).”
Is Pessimism Provably Efficient for Offline RL? (2020, background): “Such datasets are abundantly available in various domains, e.g., electronic health records for precision medicine (Chakraborty and Murphy, 2014) and human driving trajectories for autonomous driving (Sun et al., 2020).”
A Pilot SMART for Developing an Adaptive Treatment Strategy for Adolescent Depression (2015, unknown): “ATSs can be developed and refined within the context of a sequential multiple assignment randomized trial (SMART; Lavori & Dawson, 2000, 2003; Murphy, 2005).”
Near-Optimal Reinforcement Learning in Dynamic Treatment Regimes (2019, background): “Indeed, it is acknowledged in the literature [7, 8] that the “development of statistically sound estimation and inference techniques” for online RL settings “seem to be another very important research direction”, especially when the increasing use of mobiles devices allows for the possibility of con”
TREE-BASED REINFORCEMENT LEARNING FOR ESTIMATING OPTIMAL DYNAMIC TREATMENT REGIMES (2018, background): “Dynamic treatment regimes (DTRs) (Robins, 1986, 1997, 2004; Murphy, 2003; Chakraborty and Murphy, 2014) are prespecified sequences of treatment de-”
Statistical Causality from a Decision-Theoretic Perspective (2014, background): “That paper (see also Chakraborty and Murphy (2014)) set the problem up in a PR framework, assuming the simultaneous existence of potential responses (L1s, L2s, Ys) for each possible strategy σ = s, subject to certain consistency requirements, sequential ignorability then being expressed as a conditi”
Optimal medication dosing from suboptimal clinical examples: A deep reinforcement learning approach (2016, methodology): “Application of RL to design of clinical trials and adjustment of clinical treatments have been previously suggested in the literature [15], [16], [17].”
Deep Reinforcement Learning: An Overview (2017, methodology): “thods to quantify the evidence of the learned optimal policy. Goldberg and Kosorok (2012) proposed methods for censored data (patients may drop out during the trial) and ﬂexible number of stages. See Chakraborty and Murphy (2014) for a recent survey, and Kosorok and Moodie (2015) for an edited book ”
Network Randomization: A Simple Technique for Generalization in Deep Reinforcement Learning (2019, background): “Namely, RL agents often overﬁt to training environments, thus the lack of generalization ability makes them unreliable in several applications, such as health care (Chakraborty & Murphy, 2014) and ﬁnance (Deng et al., 2016).”
Sparse concordance-assisted learning for optimal treatment decision (2018, background): “Many learning algorithms have been proposed to estimate optimal treatment regime (Qian et al. (2012)).”