Background Artificial intelligence (AI)-based medical devices and digital health
technologies, including medical sensors, wearable health trackers, telemedicine, mobile …

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 …

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 …

Optimizing interpretable policies for Markov Decision Processes (MDPs) can be
computationally intractable for large-scale MDPs, eg, for monotone policies, the optimal …