Recently, Electronic Health Records (EHR) have become valuable for enhancing medical
decision making, as well as online disease detection and monitoring. Meanwhile, deep …

Abstract Clinical Electronic Health Records (EHR) data is diverse, imbalanced,
heterogeneous, and contains a lot of missing values, yet the use of EHR data is still crucial …

Deep learning methods especially recurrent neural network based models have
demonstrated early success in disease risk prediction on longitudinal patient data. Existing …

Emergency medical service (EMS) plays an essential role in increasing survival rates as it
provides first aid to victims of life-threatening emergencies. However, unbalanced EMS …

Abstract Model dynamic graphs with entity-specific heterogeneity has achieved remarkable
success across various domains. By utilizing a novel dynamic graph encoding mechanism …

This study developed a novel hybrid deep learning framework aimed at enhancing the
accuracy of disease prediction using temporal data from Electronic Health Records (EHRs) …

Healthcare monitoring systems have advanced significantly in emergency rooms and many
other health settings. Today, many nations are deeply concerned about the rise of small …

The advent of the Internet era has led to an explosive growth in the Electronic Health
Records (EHR) in the past decades. The EHR data can be regarded as a collection of …

Computational prediction of in-hospital mortality in the setting of an intensive care unit can
help clinical practitioners guide care and make early decisions for interventions. In this work …

Responding rapidly to a patient who is demonstrating signs of imminent clinical deterioration
is a basic tenet of patient care. This gave rise to a patient safety intervention philosophy …