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Found 5 papers, 1 papers with code
Fusing Individualized Treatment Rules Using Secondary Outcomes
Therefore, our objective is to learn an ITR that not only maximizes the value function for the primary outcome, but also approximates the optimal rule for the secondary outcomes as closely as possible.
Reinforcement Learning with Hidden Markov Models for Discovering Decision-Making Dynamics
To measure reward processing, patients perform computer-based behavioral tasks that involve making choices or responding to stimulants that are associated with different outcomes.
Dynamic COVID risk assessment accounting for community virus exposure from a spatial-temporal transmission model
COVID-19 pandemic has caused unprecedented negative impacts on our society, including further exposing inequity and disparity in public health.
Representation Learning for Integrating Multi-domain Outcomes to Optimize Individualized Treatment
This learning framework is based on the measurement theory in psychiatry for modeling multiple disease diagnostic measures as arising from the underlying causes (true mental states).
Representation Learning for Integrating Multi-domain Outcomes to Optimize Individualized Treatments
This learning framework is based on the measurement theory in psychiatry for modeling multiple disease diagnostic measures as arising from the underlying causes (true mental states).
