Generative, pre-trained transformers (GPTs, a type of" Foundation Models") have reshaped
natural language processing (NLP) through their versatility in diverse downstream tasks …

Drug recommendation is an important task of AI for healthcare. To recommend proper drugs,
existing methods rely on various clinical records (eg, diagnosis and procedures), which are …

Digital twin is one promising and key technology that emerged with Industry 4.0 to assist the
decision-making process in multiple industries, enabling potential benefits such as reducing …

For care of chronic diseases (eg, depression, diabetes, hypertension), it is critical to identify
effective treatment pathways that aim to promptly update the medication following the …

Due to the insufficiency of electronic health records (EHR) data utilized in practical diagnosis
prediction scenarios, most works are devoted to learning powerful patient representations …

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 …

As a subset of machine learning, meta-learning, or learning to learn, aims at improving the
model's capabilities by employing prior knowledge and experience. A meta-learning …

With the widespread adoption of electronic health records (EHR) data, deep learning
techniques have been broadly utilized for various health prediction tasks. Nevertheless, the …

Gene expression profiles obtained through DNA microarray have proven successful in
providing critical information for cancer detection classifiers. However, the limited number of …

Personalized prediction of responses for individual entities caused by external drivers is vital
across many disciplines. Recent machine learning (ML) advances have led to new state-of …