Deep learning describes a class of machine learning algorithms that are capable of
combining raw inputs into layers of intermediate features. These algorithms have recently …

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

A long-running goal of the clinical NLP community is the extraction of important variables
trapped in clinical notes. However, roadblocks have included dataset shift from the general …

Background Word embeddings have been prevalently used in biomedical Natural
Language Processing (NLP) applications due to the ability of the vector representations …

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 …

Background Large language models (LLMs) have shown remarkable capabilities in natural
language processing (NLP), especially in domains where labeled data are scarce or …

Background Automatic clinical text classification is a natural language processing (NLP)
technology that unlocks information embedded in clinical narratives. Machine learning …

There is an increased demand for detecting online hate speech, especially with the recent
changing policies of hate content and free-of-speech right of online social media platforms …

Clinical Named Entity Recognition (NER) is a critical natural language processing (NLP)
task to extract important concepts (named entities) from clinical narratives. Researchers …

This paper describes the results of SemEval 2023 task 7--Multi-Evidence Natural Language
Inference for Clinical Trial Data (NLI4CT)--consisting of 2 tasks, a Natural Language …