Electronic health records (EHRs) are becoming a vital source of data for healthcare quality
improvement, research, and operations. However, much of the most valuable information …

Objective The goal of this study is to explore transformer-based models (eg, Bidirectional
Encoder Representations from Transformers [BERT]) for clinical concept extraction and …

Biomedical named entity recognition (BNER), which extracts important named entities such
as genes and proteins, is a challenging task in automated systems that mine knowledge in …

Named entity recognition is a crucial component of biomedical natural language processing,
enabling information extraction and ultimately reasoning over and knowledge discovery …

Biomedical information extraction (BioIE) is important to many applications, including clinical
decision support, integrative biology, and pharmacovigilance, and therefore it has been an …

A large amount of detailed data about bridge conditions and maintenance actions are buried
in bridge inspection reports without being used. Information extraction and data analytics …

Objective Named entity recognition (NER) is one of the fundamental tasks in natural
language processing. In the medical domain, there have been a number of studies on NER …

Biomedical Named Entity Recognition (BNER), which extracts important entities such as
genes and proteins, is a crucial step of natural language processing in the biomedical …

Objective To develop a natural language processing system that solves both clinical concept
extraction and relation extraction in a unified prompt-based machine reading …

Objective To develop a comprehensive temporal information extraction system that can
identify events, temporal expressions, and their temporal relations in clinical text. This …