Abstract As Noncommunicable Diseases (NCDs) are affected or controlled by diverse
factors such as age, regionalism, timeliness or seasonality, they are always challenging to …

Background Accurately recognizing rare diseases based on symptom description is an
important task in patient triage, early risk stratification, and target therapies. However, due to …

With the development and maturity of machine learning methods, medical diagnosis aided
with machine learning methods has become a popular method to assist doctors in …

The representation of knowledge based on first-order logic captures the richness of natural
language and supports multiple probabilistic inference models. Although symbolic …

Objective Medical knowledge graph (KG) is attracting attention from both academic and
healthcare industry due to its power in intelligent healthcare applications. In this paper, we …

Background Many important clinical decisions require causal knowledge (CK) to take action.
Although many causal knowledge bases for medicine have been constructed, a …

The scale of medical data is growing rapidly, and these data come from different data
sources. The amount of data is huge, the production speed is fast, and the format is different …

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 …

Representation learning of medical Knowledge Graph (KG) is an important task and forms
the fundamental process for intelligent medical applications such as disease diagnosis and …

Drug discovery is of great significance in medical and biological research, while the study of
Drug-Target Interaction (DTI) and Drug-Drug Interaction (DDI) can help accelerate drug …