Introduction We aimed to assess whether machine learning models are superior at
predicting acute kidney injury (AKI) compared to logistic regression (LR), a conventional …

Kidney pathophysiology is often complex, nonlinear and heterogeneous, which limits the
utility of hypothetical-deductive reasoning and linear, statistical approaches to diagnosis and …

We study the problem of learning fair prediction models for unseen test sets distributed
differently from the train set. Stability against changes in data distribution is an important …

Objective The study sought to describe the prevalence and nature of clinical expert
involvement in the development, evaluation, and implementation of clinical decision support …

Importance Acute kidney injury (AKI) is a heterogeneous syndrome prevalent among
hospitalized patients. Personalized risk estimation and risk factor identification may allow …

Acute kidney injury (AKI), which is a common complication of acute illnesses, affects the
health of individuals in community, acute care and post-acute care settings. Although the …

Acute kidney injury (AKI) is a frequent complication in hospitalized patients, which is
associated with worse short and long-term outcomes. It is crucial to develop methods to …

The evolution of intracranial pressure (ICP) of critically ill patients admitted to a
neurointensive care unit (ICU) is difficult to predict. Besides the underlying disease and …

Kidney diseases form part of the major health burdens experienced all over the world.
Kidney diseases are linked to high economic burden, deaths, and morbidity rates. The great …

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 …