Recent years have witnessed widespread adoption of machine learning (ML)/deep learning
(DL) techniques due to their superior performance for a variety of healthcare applications …

Introduction: Artificial intelligence (AI) technologies continue to attract interest from a broad
range of disciplines in recent years, including health. The increase in computer hardware …

There is an increasing interest in developing artificial intelligence (AI) systems to process
and interpret electronic health records (EHRs). Natural language processing (NLP) powered …

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

Objective The study sought to develop and evaluate neural natural language processing
(NLP) packages for the syntactic analysis and named entity recognition of biomedical and …

Electronic health records (EHRs), digital collections of patient healthcare events and
observations, are ubiquitous in medicine and critical to healthcare delivery, operations, and …

Electronic health records (EHR) contain large volumes of unstructured text, requiring the
application of information extraction (IE) technologies to enable clinical analysis. We present …

Significant growth in Electronic Health Records (EHR) over the last decade has provided an
abundance of clinical text that is mostly unstructured and untapped. This huge amount of …

Long-form clinical summarization of hospital admissions has real-world significance
because of its potential to help both clinicians and patients. The factual consistency of …

Developing high-performance entity normalization algorithms that can alleviate the term
variation problem is of great interest to the biomedical community. Although deep learning …