As a subfield of machine learning, reinforcement learning (RL) aims at optimizing decision
making by using interaction samples of an agent with its environment and the potentially …

Discovering new treatments and personalizing existing ones is one of the major goals of
modern clinical research. In the last decade, Artificial Intelligence (AI) has enabled the …

Abstract Machine learning is a branch of computer science that has the potential to transform
epidemiologic sciences. Amid a growing focus on “Big Data,” it offers epidemiologists new …

Misdosing medications with sensitive therapeutic windows, such as heparin, can place
patients at unnecessary risk, increase length of hospital stay, and lead to wasted hospital …

This study addresses the optimal inventory management problem for new smartphone
products as an effective example of a supply chain with a short product life cycle. The …

Background and objective Cancer is one of the major causes of death worldwide and
chemotherapies are the most significant anti-cancer therapy, in spite of the emerging …

One of the main challenges in imitation learning is determining what action an agent should
take when outside the state distribution of the demonstrations. Inverse reinforcement …

Glycemic control is essential for critical care. However, it is a challenging task because there
has been no study on personalized optimal strategies for glycemic control. This work aims to …

A key challenge in e-learning environments like Intelligent Tutoring Systems (ITSs) is to
induce effective pedagogical policies efficiently. While Deep Reinforcement Learning (DRL) …

Clinical decision-making models have been developed to support therapeutic interventions
based on medical data from either a single hospital or multiple hospitals. However, models …