Insider threats, as one type of the most challenging threats in cyberspace, usually cause
significant loss to organizations. While the problem of insider threat detection has been …

Disease diagnosis is a critical task which needs to be done with extreme precision. In recent
times, medical data mining is gaining popularity in complex healthcare problems based …

Background: With the rising prevalence of diabetes, machine learning (ML) models have
been increasingly used for prediction of diabetes and its complications, due to their ability to …

Due to the discrepancy of diseases and symptoms, patients usually visit hospitals irregularly
and different physiological variables are examined at each visit, producing large amounts of …

Artificial intelligence for social good (AI4SG) is a research theme that aims to use and
advance artificial intelligence to address societal issues and improve the well-being of the …

Continuous diagnosis and prognosis are essential for critical patients. They can provide
more opportunities for timely treatment and rational allocation. Although deep-learning …

Many online platforms have deployed anti-fraud systems to detect and prevent fraudulent
activities. However, there is usually a gap between the time that a user commits a fraudulent …

Biomedical multi-modality data (also named multi-omics data) refer to data that span
different types and derive from multiple sources in clinical practices (eg gene sequences …

Accurate prognostic prediction using molecular information is a challenging area of
research, which is essential to develop precision medicine. In this paper, we develop …

Complication risk profiling is a key challenge in the healthcare domain due to the complex
interaction between heterogeneous entities (eg, visit, disease, medication) in clinical data …