Adaptive Treatment of Epilepsy via Batch-mode Reinforcement Learning
Explore this paper's citation graph
Summary
Recent techniques from the reinforcement learning literature are applied to learn an optimal stimulation policy using labeled training data from animal brain tissues for the treatment of epilepsy, and it is shown that these methods are an effective means of reducing tile incidence of seizures, while also minimizing the amount of stimulation applied.
- Type
- article
- Published
- 2008-07-13
- Cited by
- 107
- References
- 17
- OpenAlex
- https://openalex.org/W91593682
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:18170243
Keywords
Reinforcement learning, Epilepsy, Computer science, Stimulation, Artificial intelligence
References
- Epilepsy: Frequency Causes and Consequences
- Approximate solutions to markov decision processes
- Introduction to Reinforcement Learning
- Probabilistic neural network model for the in silico evaluation of anti-HIV activity and mechanism of action.
- Effectiveness of vagus nerve stimulation in epilepsy patients
- Extremely randomized trees
- Effect of an External Responsive Neurostimulator on Seizures and Electrographic Discharges during Subdural Electrode Monitoring
- Repetitive low-frequency stimulation reduces epileptiform synchronization in limbic neuronal networks.
- Reinforcement Learning: A Survey
- Tree-Based Batch Mode Reinforcement Learning
- Reinforcement Learning: An Introduction
- Temporal Difference Learning and TD-Gammon
- Clinical data based optimal STI strategies for HIV: a reinforcement learning approach
- Epilepsy in Small-World Networks
- Batch reinforcement learning in a complex domain
- Methodological Challenges in Constructing Effective Treatment Sequences for Chronic Psychiatric Disorders
- Kernel-Based Reinforcement Learning
Cited by
- Machine Learning for Seizure Prediction
- Apprentissage par renforcement batch fondé sur la reconstruction de trajectoires artificielles
- Reinforcement Learning Strategies for Clincal Trials in Non-small Cell Lung Cancer
- Developing Adaptive Personalized Therapy for Cystic Fibrosis using Reinforcement Learning
- CLEAN Learning to Improve Coordination and Scalability in Multiagent Systems
- Tree‐based reinforcement learning for optimal water reservoir operation
- A multiobjective reinforcement learning approach to water resources systems operation: Pareto frontier approximation in a single run
- Compact parametric models for efficient sequential decision making in high-dimensional, uncertain domains
- Active Learning for Developing Personalized Treatment
- Estimating the Optimal Dosage of Sodium Valproate in Idiopathic Generalized Epilepsy with Adaptive Neuro-Fuzzy Inference System
- TEXPLORE: Temporal Difference Reinforcement Learning for Robots and Time-Constrained Domains
- Hybrid Reinforcement Learning-based approach for agent motion control
- Adaptive control of epileptiform excitability in an in vitro model of limbic seizures
- Reinforcement Learning Strategies for Clinical Trials in Non-small Cell Lung Cancer
- A bistable computational model of recurring epileptiform activity as observed in rodent slice preparations
- Treating Epilepsy via Adaptive Neurostimulation: a Reinforcement Learning Approach
- Comments on: Optimization and data mining in medicine
- An object-oriented representation for efficient reinforcement learning
- Reinforcement learning for closed-loop propofol anesthesia: a study in human volunteers
- Convergent Fitted Value Iteration with Linear Function Approximation
Related papers
- Reinforcement Learning: An Introduction
- Tree-Based Batch Mode Reinforcement Learning
- Dynamic Programming
- Informing sequential clinical decision-making through reinforcement learning: an empirical study
- Least-Squares Policy Iteration
- Extremely randomized trees
- Learning from delayed rewards
- Optimal dynamic treatment regimes