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: Natural Language Processing (NLP) methods are increasingly being utilized to
mine knowledge from unstructured health-related texts. Recent advances in noisy text …

Federated learning is a privacy-preserving machine learning technique to train intelligent
models from decentralized data, which enables exploiting private data by communicating …

Exponential growth of biomedical literature and clinical data demands more robust yet
precise computational methodologies to extract useful insights from biomedical literature …

Millions of people are affected by retinal abnormalities worldwide. Early detection and
treatment of these abnormalities could arrest further progression, saving multitudes from …

Traditional machine learning methods used to detect the side effects of drugs pose
significant challenges as feature engineering processes are labor-intensive, expert …

Background Social media use is now ubiquitous, but the growth in social media
communications has also made it a convenient digital platform for drug dealers selling …

The primary goal of drug safety researchers and regulators is to promptly identify adverse
drug reactions. Doing so may in turn prevent or reduce the harm to patients and ultimately …

In this study, we developed a hybrid deep learning (DL) model, which is one of the first
interpretable hybrid DL models with Inception modules, to give a descriptive prediction of …

Background Accurate and timely detection of medical adverse events (AEs) from free-text
medical narratives can be challenging. Natural language processing (NLP) with deep …