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 …

Objective Temporal electronic health records (EHRs) contain a wealth of information for
secondary uses, such as clinical events prediction and chronic disease management …

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

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

Background Sepsis is a life-threatening syndrome eliciting highly heterogeneous host
responses. Current prognostic evaluation methods used in clinical practice are …

Machine learning algorithms have had a profound impact on the field of computer science
over the past few decades. The performance of these algorithms heavily depends on the …

Background: Developing a decision support system based on advances in machine learning
is one area for strategic innovation in healthcare. Predicting a patient's progression to septic …

In a hospital environment, patients are monitored continuously by electronic devices and
health professionals. Therefore, a large amount of data is collected and stored in electronic …

Infections encompass a set of medical conditions of very diverse kinds that can pose a
significant risk to health, and even death. As with many other diseases, early diagnosis can …

Temporal pattern mining can be employed to detect patterns and trends in a patient's health
status as it evolves over time. However, these methods often produce an overwhelming …