Objective This article methodically reviews the literature on deep learning (DL) for natural
language processing (NLP) in the clinical domain, providing quantitative analysis to answer …

The past decade has witnessed an explosion of textual information in the biomedical field.
Biomedical texts provide a basis for healthcare delivery, knowledge discovery, and decision …

A binary classification problem is common in medical field, and we often use sensitivity,
specificity, accuracy, negative and positive predictive values as measures of performance of …

News in social media, such as Twitter, has been generated in high volume and speed.
However, very few of them are labeled (as fake or true news) by professionals in near real …

Abstract Named Entity Recognition is the process of identifying different entities in a given
context. Biomedical Named Entity Recognition (BNER) is the task of extracting chemical …

The use of BERT, one of the most popular language models, has led to improvements in
many Natural Language Processing (NLP) tasks. One such task is Named Entity …

In this paper, we propose an attention-based multi-task neural network model for text
classification and sequence tagging and then apply it to the named entity recognition and …

Because of difficulty processing the electronic medical record data of patients with
cerebrovascular disease, there is little mature recognition technology capable of identifying …

In recent years, there have been major advances in deep learning algorithms for image
recognition in traumatic brain injury (TBI). Interest in this area has increased due to the …

Background. Clinical named entity recognition is the basic task of mining electronic medical
records text, which are with some challenges containing the language features of Chinese …