Disease prediction based on patients' historical admission records is an essential task in the
medical field, but current predictive models often lack interpretability, which is a critical …

Predicting the future health conditions of patients based on Electronic Health Records (EHR)
is an important research topic. Due to the temporal nature of EHR data, the major challenge …

Objective: To combine medical knowledge and medical data to interpretably predict the risk
of disease. Methods: We formulated the disease progression prediction task as a random …

The goal of diagnosis prediction task is to predict the future health information of patients
from their historical Electronic Healthcare Records (EHR). The most important and …

Diagnosis prediction is an important predictive task in health care that aims to predict the
patient future diagnosis based on their historical medical records. A crucial requirement for …

Clinical predictive models often rely on patients' electronic health records (EHR), but
integrating medical knowledge to enhance predictions and decision-making is challenging …

In recent years, with the better availability of medical data such as Electronic Health Records
(EHR), more and more data mining models have been developed to explore the data-driven …

While recent developments of deep learning models have led to record-breaking
achievements in many areas, the lack of sufficient interpretation remains a problem for many …

Thanks to the widespread adoption of Electronic Health Record (EHR) systems, a variety of
data-driven clinical risk prediction approaches have been spawned in recent years …

Deep learning techniques are capable of capturing complex input-output relationships, and
have been widely applied to the diagnosis prediction task based on web-based patient …