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 …

Introduction Identifying patient safety events using electronic health records (EHRs) and
automated machine learning-based detection methods can help improve the efficiency and …

Objective In acupuncture therapy, the accurate location of acupoints is essential for its
effectiveness. The advanced language understanding capabilities of large language models …

In this study, we developed a hybrid deep learning (DL) model, which is one of the first
interpretable hybrid DL models with Inception modules, to give a descriptive prediction of …

In biomedical literature, cross-sentence texts can usually express rich knowledge, and
extracting the interaction relation between entities from cross-sentence texts is of great …

Analyzing adverse drug events (ADEs) is an integral part of drug safety monitoring, which
plays a significant role in medication decision-making. The increasing prevalence of health …

Many studies have demonstrated that incorporating lexical information into characters can
effectively improve the performance of Chinese Named Entity Recognition (CNER) …

Introduction Pharmacovigilance (PV) involves monitoring and aggregating adverse event
information from a variety of data sources, including health records, biomedical literature …

Background An adverse drug event (ADE) is any unfavorable effect that occurs due to the
use of a drug. Extracting ADEs from unstructured clinical notes is essential to biomedical text …

Biomedical relation extraction from scientific publications is a key task in biomedical natural
language processing (NLP) and can facilitate the creation of large knowledge bases, enable …