Here we present deep-learning techniques for healthcare, centering our discussion on deep
learning in computer vision, natural language processing, reinforcement learning, and …

Purpose Early clinical recognition of sepsis can be challenging. With the advancement of
machine learning, promising real-time models to predict sepsis have emerged. We …

In medicine, artificial intelligence (AI) research is becoming increasingly focused on
applying machine learning (ML) techniques to complex problems, and so allowing …

Sepsis is defined as organ dysfunction resulting from the host's deleterious response to
infection. One of the most common organs affected is the kidneys, resulting in sepsis …

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 …

Severe or life threatening infections are common among patients in the intensive care unit
(ICU). Most infections in the ICU are bacterial or fungal in origin and require antimicrobial …

Over the last years, there have been advances in the use of data-driven techniques to
improve the definition, early recognition, subtypes characterisation, prognostication and …

Sepsis-associated acute kidney injury (S-AKI) is a common, life-threatening complication in
hospitalized and critically ill patients. S-AKI increases in-hospital mortality 6-fold to 8-fold, 1 …

Objectives: Develop and implement a machine learning algorithm to predict severe sepsis
and septic shock and evaluate the impact on clinical practice and patient outcomes. Design …

Abstract Background Intensive care units (ICUs) face financial, bed management, and
staffing constraints. Detailed data covering all aspects of patients' journeys into and through …