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 …

Clinical data management systems typically provide caregiver teams with useful information,
derived from large, sometimes highly heterogeneous, data sources that are often changing …

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 …

The explosion of digital healthcare data has led to a surge of data-driven medical research
based on machine learning. In recent years, as a powerful technique for big data, deep …

Reinforcement learning (RL) can assist in medical decision making using patient data
collected in electronic health record (EHR) systems. RL, a type of machine learning, can use …

The field of precision medicine explores disease treatments by looking at genetic, socio-
environmental, and clinical factors, thus trying to provide a holistic view of a person's health …

Many recent articles highlight the data revolution in healthcare, an offshoot of the vast
amount of digital medical information that has now accumulated in electronic medical …

The digitalization of the Intensive Care Unit (ICU) led to an increasing amount of clinical data
being collected at the bedside. The term “Big Data” can be used to refer to the analysis of …

Objective Machine learning techniques have demonstrated superior discrimination
compared to conventional statistical approaches in predicting trauma death. The objective of …

Intensive care is an ideal environment for the use of Big Data Analysis (BDA) and Machine
Learning (ML), due to the huge amount of information processed and stored in electronic …