Abstract Machine learning is frequently being leveraged to tackle problems in the health
sector including utilization for clinical decision-support. Its use has historically been focused …

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 …

Today, despite decades of developments in medicine and the growing interest in precision
healthcare, vast majority of diagnoses happen once patients begin to show noticeable signs …

Learning multimodal representations involves integrating information from multiple
heterogeneous sources of data. It is a challenging yet crucial area with numerous real-world …

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

As natural language processing methods are increasingly deployed in real-world scenarios
such as healthcare, legal systems, and social science, it becomes necessary to recognize …

Much of the knowledge and information needed for enabling high-quality clinical research is
stored in free-text format. Natural language processing (NLP) has been used to extract …

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 …

Electronic health record systems are ubiquitous and the majority of patients' data are now
being collected electronically in the form of free text. Deep learning has significantly …

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 …