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 …

Internet forums and public social media, such as online healthcare forums, provide a
convenient channel for users (people/patients) concerned about health issues to discuss …

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 …

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 …

Objectives: We survey recent developments in medical Information Extraction (IE) as
reported in the literature from the past three years. Our focus is on the fundamental …

Distributed vector representations or embeddings map variable length text to dense fixed
length vectors as well as capture prior knowledge which can transferred to downstream …

Background: Clinical named entity recognition (CNER), whose goal is to automatically
identify clinical entities in electronic medical records (EMRs), is an important research …

Abstract Electronic Medical Records (EMRs) are written in an unstructured way, often using
natural language. Information Extraction (IE) may be used for acquiring knowledge from …

Artificial Neural networks (ANN) are composed of nodes that are joint to each other through
weighted connections. Deep learning, as an extension of ANN, is a neural network model …