The early prediction of deterioration could have an important role in supporting healthcare
professionals, as an estimated 11% of deaths in hospital follow a failure to promptly …

Background The number of studies applying machine learning (ML) to predict acute kidney
injury (AKI) has grown steadily over the past decade. We assess and critically appraise the …

Early diagnosis of critically ill patients depends on the attention and observation of medical
staff about different variables, as vital signs, results of laboratory tests, among other …

Background: Without timely diagnosis and treatment, tachycardia, also called
tachyarrhythmia, can cause serious complications such as heart failure, cardiac arrest, and …

Medical applications of machine learning (ML) have experienced a surge in popularity in
recent years. The intensive care unit (ICU) is a natural habitat for ML given the abundance of …

Wildfires are becoming an increasing challenge to the sustainability of boreal peatland (BP)
ecosystems and can alter the stability of boreal carbon storage. However, predicting the …

Objectives Acute kidney injury (AKI) is a sudden episode of kidney failure or damage and
the risk of AKI is determined by the complex interactions of patient factors. In this study, we …

Acute kidney injury (AKI) is a sudden onset of kidney damage. Currently, there is no hybrid
model predicting AKI that takes advantage of two types of data. In this research, a novel …

Wildfires are becoming an increasing challenge to the sustainability of boreal peatland (BP)
ecosystems and can alter the stability of boreal carbon storage. However, a quantitative …

Acute kidney injury (AKI) is a common and potentially life-threatening condition, which often
occurs in the intensive care unit. We propose a machine learning model based on recurrent …