Time series forecasting has become a very intensive field of research, which is even
increasing in recent years. Deep neural networks have proved to be powerful and are …

Background: Sepsis is among the leading causes of death in intensive care units (ICUs)
worldwide and its recognition, particularly in the early stages of the disease, remains a …

Acute critical illness is often preceded by deterioration of routinely measured clinical
parameters, eg, blood pressure and heart rate. Early clinical prediction is typically based on …

Objective Temporal electronic health records (EHRs) contain a wealth of information for
secondary uses, such as clinical events prediction and chronic disease management …

An increasing number of machine learning models have been deployed in domains with
high stakes such as finance and healthcare. Despite their superior performances, many …

Machine learning is increasingly integrated into clinical practice, with applications ranging
from pre-clinical data processing, bedside diagnosis assistance, patient stratification …

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 …

Multiple organ failure is the trademark of sepsis. Sepsis occurs when the body's reaction to
infection causes injury to its tissues and organs. As a consequence, fluid builds up in the …

Background: Sepsis, a life-threatening infection-induced inflammatory condition, has
significant global health impacts. Timely detection is crucial for improving patient outcomes …

Recent improvements in the IoT are giving rise to the explosion of interconnected devices,
empowering many smart applications. IoT devices engender massive data that requires …