Background: Clinical narratives represent the main form of communication within health
care, providing a personalized account of patient history and assessments, and offering rich …

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 …

Objective Neural network–based representations (“embeddings”) have dramatically
advanced natural language processing (NLP) tasks, including clinical NLP tasks such as …

Abstract Clinical Named Entity Recognition (CNER) is a critical task which aims to identify
and classify clinical terms in electronic medical records. In recent years, deep neural …

The extensive growth of data in the health domain has increased the utility of Deep Learning
in health. Deep learning is a highly advanced successor of artificial neural networks, having …

Background Concept extraction, a subdomain of natural language processing (NLP) with a
focus on extracting concepts of interest, has been adopted to computationally extract clinical …

Objective Breast cancer is the most common malignant tumor among women. The diagnosis
and treatment information of breast cancer patients is abundant in multiple types of clinical …

In this work, we conducted a visualized bibliometric analysis to map the research trends of
machine learning in engineering (MLE) based on articles indexed in the Web of Science …

The past decade has seen an explosion of the amount of digital information generated
within the healthcare domain. Digital data exist in the form of images, video, speech …

Semantic processing is a fundamental research domain in computational linguistics. In the
era of powerful pre-trained language models and large language models, the advancement …