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 …

Dynamic opinion maximization (DOM) is a significant optimization issue, whose target is to
select some nodes in the network and prorogate the opinions of network nodes, and …

The conventional approach for controlling the supply temperature in collective space
heating networks relies on a predefined heating curve determined by outdoor temperature …

Introduction: Develo** reliable medication dosing guidelines is challenging because
individual dose–response relationships are mitigated by both static (eg, demographic) and …

Common treatments in Intensive Care Units frequently involve prolonged sedation.
Maintaining adequate sedation levels is challenging and prone to errors including: incorrect …

Weaning patients from mechanical ventilators is a crucial decision in intensive care units
(ICUs), significantly affecting patient outcomes and the throughput of ICUs. This study aims …

Health-related data is noisy and stochastic in implying the true physiological states of
patients, limiting information contained in single-moment observations for sequential clinical …

There is an emerging trend in the reinforcement learning for healthcare literature. In order to
prepare longitudinal, irregularly sampled, clinical datasets for reinforcement learning …

The process of deciding when to extubate patients who rely on mechanical ventilation for
breathing is critical in intensive care settings. This research focuses on determining the …