The global growth of social media usage over the past decade has opened research
avenues for mining health related information that can ultimately be used to improve public …

In this paper, we focus on the classification of tweets as sources of potential signals for
adverse drug effects (ADEs) or drug reactions (ADRs). Following the intuition that text and …

Detection of occupations in texts is relevant for a range of important application scenarios,
like competitive intelligence, sociodemographic analysis, legal NLP or health-related …

Objective The aim of the Social Media Mining for Health Applications (# SMM4H) shared
tasks is to take a community-driven approach to address the natural language processing …

Abstract Adverse Drug Event (ADE) extraction is one of the core tasks in digital
pharmacovigilance, especially when applied to informal texts. This task has been addressed …

The COVID-19 pandemic continues to bring up various topics discussed or debated on
social media. In order to explore the impact of pandemics on people's lives, it is crucial to …

Abstract Objectives Large language models (LLMs) have demonstrated remarkable success
in natural language processing (NLP) tasks. This study aimed to evaluate their …

Background Adverse reactions to drugs attract significant concern in both clinical practice
and public health monitoring. Multiple measures have been put into place to increase …

Early detection and management of adverse drug reactions (ADRs) is crucial for improving
patients' quality of life. Hand-foot syndrome (HFS) is one of the most problematic ADRs for …

Twitter is a valuable source of patient-generated data that has been used in various
population health studies. The first step in many of these studies is to identify and capture …