Reinforcement learning for healthcare

Reinforcement learning is a branch of artificial intelligence that provides methods to optimize sequential decisions for long-term outcomes. It has many potential applications in healthcare, such as improving treatment strategies, enhancing patient safety, and reducing costs.

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Possible topics

• Contextual Bayesian non-stationary treatment recommendations
• Data safe havens, federated learning and other privacy-enhancing techniques
• Improving human board game performance through reinforcement learning
• Reinforcement learning for complex adaptive individual health interventions
• Simulation of patient data for micro-randomized trials

Further reading

• Shrestha, S, Jain, S. A Bayesian-bandit adaptive design for N-of-1 clinical trials. Statistics in Medicine. 2021; 40: 1825– 1844. https://doi.org/10.1002/sim.8873