Clinical data based optimal STI strategies for HIV: a reinforcement learning approach

By Damien Ernst, Guy-Bart Vincent Stan, Jorge M. Goncalves, Louis Wehenkel. 2006

153 citations recorded by the source corpus.

Subfields: Reinforcement Learning, Medical & Health AI

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Citations in the corpus

Reinforcement Learning in Healthcare: A Survey (2019, methodology): “dividuals in their immune responses to treatment, discovering the optimal drug combinations and scheduling strategy is still a difﬁcult task in both medical research and clinical trials. Ernst et al. [145] ﬁrst introduced RL techniques in computing Structured Treatment Interruption (STI) strategies ”
Combining Kernel and Model Based Learning for HIV Therapy Selection (2017, methodology): “While reinforcement learning has been used to optimise HIV treatment strategies in simulation studies [7, 8], reasoning about possible futures from limited observational data can make these approaches brittle in practice.”
Informing sequential clinical decision-making through reinforcement learning: an empirical study (2010, background): “There has been a surge of interest in recent years within the medical community regarding the application of Reinforcement Learning (RL) techniques to optimize treatment policies (Thall and Wathan 2000; Ernst et al. 2006; Murphy et al. 2007; Guez et al. 2008; Zhao et al. 2009).”
Off-Policy Policy Gradient with Stationary Distribution Correction (2019, methodology): “The second domain is an HIV treatment simulation described in (Ernst et al., 2006).”
Batch Mode Reinforcement Learning based on the Synthesis of Artificial Trajectories (2012, background): “…Bonarini et al. 2008; Tognetti et al. 2009), power systems (Ernst et al. 2009), image processing (Ernst et al. 2006a), water reservoir optimization (Castelletti et al. 2007, 2010), medicine (Murphy 2003; Ernst et al. 2006b; Guez et al. 2008) and driving assistance strategies (Pietquin et al. 2011).”
Representation Balancing MDPs for Off-Policy Policy Evaluation (2018, background): “[6], and consists of 6 parameters describing the state of the patient and 4 possible actions.”
Towards Optimal Off-Policy Evaluation for Reinforcement Learning with Marginalized Importance Sampling (2019, background): “…advertising and marketing [Bottou et al., 2013; Tang et al., 2013; Chapelle et al., 2015; Theocharous et al., 2015; Thomas et al., 2017] or medical treatments [Murphy et al., 2001; Ernst et al., 2006; Raghu et al., 2017], online policy evaluation is usually expensive, risky, or even unethical.”
Adaptive Control of Epileptic Seizures using Reinforcement Learning (2010, background): “The Extremely Randomized (Extra) trees [26] is a regressor with attractive properties that has already been employed in the context of a medical application [23].”
Incorporating causal factors into reinforcement learning for dynamic treatment regimes in HIV (2019, methodology): “Ernst et al. [9] first introduced RL techniques in computing Structured Treatment Interruption (STI) strategies for HIV infected patients.”
A Reinforcement Learning Design for HIV Clinical Trials (2014, methodology): “Perhaps the most significant challenge that arises when using real data is the problem of partial observability (Ernst et al., 2006).”
Reinforcement learning for closed-loop propofol anesthesia: a study in human volunteers (2014, background): “…and reported applications include ultrasound image segmentation (Sahba et al., 2008) and planning tasks, such as scheduling of HIV therapy (Ernst et al., 2006), optimizing deep-brain stimulation in epilepsy treatment (Guez et al., 2008), dosing strategies for anemia management in patients…”
The Actor Search Tree Critic (ASTC) for Off-Policy POMDP Learning in Medical Decision Making (2018, methodology): “Automatic treatment optimisation based on reinforcement learning has been explored in simulated patients for HIV therapy [4] using fitted-Q iteration, dynamic insulin dosage in diabetes using model-based reinforcement learning [5], and anaesthesia depth control using actor-critic [6].”
Optimization of anemia treatment in hemodialysis patients via reinforcement learning (2014, background): “Recently there has been a growing interest in appl ying FQI to optimize the treatment of several diseases includ ing HIV /AIDS [34], psychiatric disorders [35], epilepsy [36, 37], schizophrenia [38, 39] or smoking addiction [40].”
Reinforcement learning design for cancer clinical trials (2009, unknown): “ERT has been recently demonstrated in a simulation of HIV infection [35] and adaptive treatment of Epilepsy [36].”
Model-based Reinforcement Learning for Semi-Markov Decision Processes with Neural ODEs (2020, background): “Establishing effective treatment strategies for HIV-infected patients based on markers from blood tests can be cast as an RL problem [Ernst et al., 2006, Parbhoo et al., 2017, Killian et al., 2017].”
Direct Policy Transfer via Hidden Parameter Markov Decision Processes (2018, background): “HIV treatment.”