We introduce the Blackwell discount factor for Markov Decision Processes (MDPs). Classical objectives for MDPs include discounted, average, and Blackwell optimality. Many existing …
Purpose According to the oncologist, a single medication is insufficient to completely cure the disease; as a result, most patients undergo treatment from two or more types of therapy …
Y Wu, H Liu, K Ren, X Chang - arXiv preprint arXiv:2310.06746, 2023 - arxiv.org
Interpretability is a key concern in estimating heterogeneous treatment effects using machine learning methods, especially for healthcare applications where high-stake …
Background. Unintended biases introduced by optimization and machine learning (ML) models are of great interest to medical professionals. Bias in healthcare decisions can …
Preventing chronic diseases is an essential aspect of medical care. To prevent chronic diseases, physicians focus on monitoring their risk factors and prescribing the necessary …
Treatment and screening problems are a class of sequential decision-making problems under uncertainty that are ubiquitous in healthcare. Treatment problems aim to determine …
Access to electronic health records creates an opportunity to build stochastic models that support healthcare providers' decisions to prevent chronic diseases. As the patient's health …
SJ Lee, X Gong, GG Garcia - optimization-online.org
Optimizing interpretable policies for Markov Decision Processes (MDPs) can be computationally intractable for large-scale MDPs, eg, for monotone policies, the optimal …