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 …

Adverse drug reactions (ADRs) are a major concern for patients, clinicians, and regulatory
agencies. The discovery of serious ADRs leading to substantial morbidity and mortality has …

The mining of Tweets to develop datasets on recent issues, global challenges, pandemics,
virus outbreaks, emerging technologies, and trending matters has been of significant interest …

The vast amount of data on social media presents significant opportunities and challenges
for utilizing it as a resource for health informatics. The fifth iteration of the Social Media …

Objective Research on pharmacovigilance from social media data has focused on mining
adverse drug events (ADEs) using annotated datasets, with publications generally focusing …

Despite huge technological advances in the capabilities to capture, store, link and analyse
data electronically, there has been some but limited impact on routine pharmacovigilance …

Background This work deals with Natural Language Processing applied to Electronic Health
Records (EHRs). EHRs are coded following the International Classification of Diseases …

Neurologic disability level at hospital discharge is an important outcome in many clinical
research studies. Outside of clinical trials, neurologic outcomes must typically be extracted …

The prominence of social media in contemporary society has extended significantly into the
health care arena, where both patients and health care providers have used social media …

Highlights•Quantitative social media listening (QSML) is an emerging tool for observational
studies.•QSML can leverage advances in AI and RWD analysis to produce new patient …