Record linkage is the challenging task of deciding which records, coming from disparate
data sources, refer to the same entity. Established back in 1946 by Halbert L. Dunn, the area …

With the rapid development of computer software and hardware technologies, more and
more healthcare data are becoming readily available from clinical institutions, patients …

Recent developments in deep learning have contributed to numerous success stories in
healthcare. The performance of a deep learning model generally improves with the size of …

In data-driven medical research, multi-center studies have long been preferred over single-
center ones due to a single institute sometimes not having enough data to obtain sufficient …

Deep learning (DL) models typically require large-scale, balanced training data to be robust,
generalizable, and effective in the context of healthcare. This has been a major issue for …

With the growing concerns on data security and user privacy, a decentralized mechanism is
implemented for federated data mining (FDM), which can bridge data silos and collaborate …

With the rapid growth and widespread application of electronic health records (EHRs),
similar patient retrieval has become an important task for downstream clinical decision …

Artificial intelligence and machine learning models are key tools in advancing data-driven
healthcare solutions that aim to improve patient care and outcomes. A key step in …

Semantic text matching, which matches target texts to source texts, is a general problem in
many areas, such as information retrieval, question answering, and recommendation. The …

Federated recommender systems (FRSs), with their improved privacy-preserving
advantages to jointly train recommendation models from numerous devices while keeping …