Studies for sepsis prediction using machine learning are developing rapidly in medical
science recently. In this review, we propose a set of new evaluation criteria and reporting …

Sepsis is one of the main causes of death in critically ill patients. Despite the continuous
development of medical technology in recent years, its morbidity and mortality are still high …

Background COVID-19 remains a major healthcare concern. Vital signs are routinely
measured on admission and may provide an early, cost-effective indicator of outcome–more …

We present an interpretable machine learning algorithm called 'eARDS'for predicting ARDS
in an ICU population comprising COVID-19 patients, up to 12-hours before satisfying the …

Sepsis is a leading cause of mortality and early identification improves survival. With
increasing digitalization of health care data automated sepsis prediction models hold …

Introduction Septic patients requiring intensive care unit (ICU) readmission are at high risk of
mortality, but research focusing on the association of ICU readmission due to sepsis and …

Background Early recognition and treatment of sepsis are crucial for improving patient
outcomes. However, the diagnosis of sepsis remains challenging because of vague clinical …

Machine Learning (ML) is an algorithm based on big data, which learns patterns from the
previously observed data through classifying, predicting, and optimizing to accomplish …

We present a novel setup for treating sepsis using distributional reinforcement learning (RL).
Sepsis is a life-threatening medical emergency. Its treatment is considered to be a …

Purpose To build machine learning models for predicting the risk of in-hospital death in
patients with sepsis within 48 h, using only dynamic changes in the patient's vital signs …