Objective: Artificial intelligence and machine learning are transforming many fields including
medicine. In diabetes, robust biosensing technologies and automated insulin delivery …

Explainable reinforcement learning (XRL) is an emerging subfield of explainable machine
learning that has attracted considerable attention in recent years. The goal of XRL is to …

Diabetes mellitus is a well-known chronic disease that diminishes the insulin producing
capability of the human body. This results in high blood sugar level which might lead to …

An optimal antimicrobial dose provides enough drug to achieve a clinical response while
minimizing toxicity and development of drug resistance. There can be considerable …

Abstract Electronic Health Records (EHRs) have become one of the main sources of
evidence to evaluate clinical actions, improve medical quality, predict disease-risk, and …

Abstract Deep Reinforcement Learning (DRL) has been increasingly attempted in assisting
clinicians for real-time treatment of sepsis. While a value function quantifies the performance …

People with Type 1 diabetes (T1D) require regular exogenous infusion of insulin to maintain
their blood glucose concentration in a therapeutically adequate target range. Although 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 …

The widespread adoption of effective hybrid closed loop systems would represent an
important milestone of care for people living with type 1 diabetes (T1D). These devices …

Diabetes is a silent illness that is endangering public health in society. Diabetes is a chronic
disease affecting millions of people worldwide, and understanding the underlying …