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Found 4 papers, 2 papers with code
Leveraging Factored Action Spaces for Efficient Offline Reinforcement Learning in Healthcare
We study the theoretical properties of our approach, identifying scenarios where it is guaranteed to lead to zero bias when used to approximate the Q-function.
Disparate Censorship & Undertesting: A Source of Label Bias in Clinical Machine Learning
Using such biased labels in standard ML pipelines could contribute to gaps in model performance across patient groups.
Deep Learning Applied to Chest X-Rays: Exploiting and Preventing Shortcuts
This paper studies the case of spurious class skew in which patients with a particular attribute are spuriously more likely to have the outcome of interest.
Clinician-in-the-Loop Decision Making: Reinforcement Learning with Near-Optimal Set-Valued Policies
We analyze the theoretical properties of the proposed algorithm, providing optimality guarantees and demonstrate our approach on simulated environments and a real clinical task.
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