Transfer Learning Across Patient Variations with Hidden Parameter Markov Decision Processes

By Taylor W. Killian, George Dimitri Konidaris, Finale Doshi-Velez. 2016

9 citations recorded by the source corpus.

Due to physiological variation, patients diagnosed with the same condition may exhibit divergent, but related, responses to the same treatments. Hidden Parameter Markov Decision Processes (HiP-MDPs) tackle this transfer-learning problem by embedding these tasks into a low-dimensional space. However, the original formulation of HiP-MDP had a critical flaw: the embedding uncertainty was modeled independently of the agent's state uncertainty, requiring an unnatural training procedure in which all tasks visited every part of the state space---possible for robots that can be moved to a particular location, impossible for human patients. We update the HiP-MDP framework and extend it to more robustly develop personalized medicine strategies for HIV treatment.

Subfields: Artificial Intelligence

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In Artificial Intelligence

1. Chao Yu
2. Jiming Liu
3. Shamim Nemati
4. Guoqi Ren
5. Yinzhao Dong

Citations in the corpus

• Reinforcement Learning in Healthcare: A Survey (2019, methodology): “[106, 107], who used a Gaussian Process latent variable model for HIV treatment by inferring the transition dynamics within a task instance and the transfer between task instances.”
• Incorporating causal factors into reinforcement learning for dynamic treatment regimes in HIV (2019, background): “Killian et al. [13] similarly attempt to address and identify the variations across subpopulations in the development of HIV treatment policies by transferring knowledge between task instances.”