Deep neural networks (DNN) have fundamentally revolutionized the artificial intelligence
(AI) field. The transformer model is a type of DNN that was originally used for the natural …

With advancements in large language models, artificial intelligence (AI) is undergoing a
paradigm shift where AI models can be repurposed with minimal effort across various …

Pretraining has proven to be a powerful technique in natural language processing (NLP),
exhibiting remarkable success in various NLP downstream tasks. However, in the medical …

The task of automatic ICD (International Classification of Diseases) coding involves
allocating appropriate ICD codes to electronic health records. Due to the long-tailed …

Pathological images and molecular omics are important information for predicting diagnosis
and prognosis. The two kinds of heterogeneous modal data contain complementary …

Since the beginning of the internet era, there has been an explosion of growth in structured
data (such as numbers, symbols, and labels) as well as unstructured data (including images …

Electronic Health Records (EHRs) have become increasingly popular to support clinical
decision-making and healthcare in recent decades. EHRs usually contain heterogeneous …

Clinical diagnosis prediction models, when provided with a patient's medical history, aim to
detect potential diseases early, facilitating timely intervention and improving prognostic …

Abstract Electronic Health Records (EHRs) have transformed healthcare by digitally
consolidating patient medical history, encompassing structured data (eg, demographic data …

The rapid growth of medical data presents opportunities and challenges for healthcare
professionals and researchers. To effectively process and analyze this complex and …