Interventional pharmacology is one of medicine's most potent weapons against disease.
These drugs, however, can result in damaging side effects and must be closely monitored …

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 …

Abstract Knowledge of the underpinnings of cancer initiation, progression and metastasis
has increased exponentially in recent years. Advanced “omics” coupled with machine …

The use of deep learning (DL) for the analysis and diagnosis of biomedical and health care
problems has received unprecedented attention in the last decade. The technique has …

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 …

To reduce adverse drug events (ADEs), hospitals need a system to support them in
monitoring ADE occurrence routinely, rapidly, and at scale. Natural language processing …

Objective Identification of drugs, associated medication entities, and interactions among
them are crucial to prevent unwanted effects of drug therapy, known as adverse drug events …

Background Natural language processing (NLP) is an important traditional field in computer
science, but its application in medical research has faced many challenges. With the …

Background More than 18 million Americans are currently suffering from alcohol use
disorder (AUD): a compulsive behavior of alcohol use as a result of a chronic, relapsing …

Context.—Recent developments in machine learning have stimulated intense interest in
software that may augment or replace human experts. Machine learning may impact …