Objective: This study aims to comprehensively review the use of graph neural networks
(GNNs) for clinical risk prediction based on electronic health records (EHRs). The primary …

The explosive growth and widespread accessibility of medical information on the Internet
have led to a surge of research activity in a wide range of scientific communities including …

Background Diagnostic or procedural coding of clinical notes aims to derive a coded
summary of disease-related information about patients. Such coding is usually done …

Despite the remarkable progress in the development of predictive models for healthcare,
applying these algorithms on a large scale has been challenging. Algorithms trained on a …

Sequential diagnosis prediction on the Electronic Health Record (EHR) has been proven
crucial for predictive analytics in the medical domain. EHR data, sequential records of a …

Cold-start diagnosis prediction is a challenging task for AI in healthcare, where often only a
few visits per patient and a few observations per disease can be exploited. Although meta …

Clinical risk prediction based on Electronic Health Records (EHR) can assist doctors in
better judgment and can make sense of early diagnosis. However, the prediction …

Survival analysis plays a crucial role in many healthcare decisions, where the risk prediction
for the events of interest can support an informative outlook for a patient's medical journey …

In intensive care units (ICUs), mortality prediction is performed by combining information
from these two sources of ICU patients by monitoring patient health. Respectively, time …

As the volume of Electronic Health Records (EHR) sharply grows, there has been emerging
interest in learning the representation of EHR for healthcare applications. Representation …