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 …

Background Predictive models have been used in clinical care for decades. They can
determine the risk of a patient developing a particular condition or complication and inform …

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

Problem framing is critical to developing risk prediction models because all subsequent
development work and evaluation takes place within the context of how a problem has been …

Sepsis is a life-threatening condition caused by an exaggerated reaction of the body to an
infection, that leads to organ failure or even death. Since sepsis can kill a patient even in just …

Background The design of computerized systems able to support automated detection of
threatening conditions in critically ill patients such as systemic inflammatory response …

Sepsis is a leading cause of mortality in the intensive care unit. Early prediction of sepsis
can reduce the overall mortality rate and cost of sepsis treatment. Some studies have …

Background and objectives Sepsis is a life-threatening condition that is associated with
increased mortality. Artificial intelligence tools can inform clinical decision making by …

Interactions between pattern recognition receptors (PRRs) shape innate immune responses
to particular classes of pathogens. Here, we review interactions between TLRs and …

This paper presents a novel method for hierarchical analysis of machine learning algorithms
to improve predictions of at risk patients, thus further enabling prompt therapy. Specifically …