ArticleJournal of the American Medical Informatics Association : JAMIA2024
Overview of the 8th Social Media Mining for Health Applications (#SMM4H) shared tasks at the AMIA 2023 Annual Symposium.
Article in Journal of the American Medical Informatics Association : JAMIA, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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15 citing papers in PubMed.
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- Transformer-based models for ADR detection: cross-drug validation and benchmarking against large language models.Therapeutic advances in drug safety · 2025Article
- Evaluating large language models for health-related text classification tasks with public social media data.Journal of the American Medical Informatics Association : JAMIA · 2024Article
- Natural Language Processing for Digital Health in the Era of Large Language Models.Yearbook of medical informatics · 2024Review
- Bringing the Public Health Informatics and Technology Workforce Together: The PHIAT Conference.Online journal of public health informatics · 2024Article
- Potential of artificial intelligence in injury prevention research and practice.Injury prevention : journal of the International Society for Child and Adolescent Injury Prevention · 2024Article
- BERT-based language model for accurate drug adverse event extraction from social media: implementation, evaluation, and contributions to pharmacovigilance practices.Frontiers in public health · 2024Article
- Predicting U.S. county opioid poisoning mortality from multi-modal social media and psychological self-report data.Scientific reports · 2023Article
- An analysis of entity normalization evaluation biases in specialized domains.BMC bioinformatics · 2023Article
- Automatic Extraction of Medication Mentions from Tweets-Overview of the BioCreative VII Shared Task 3 Competition.Database : the journal of biological databases and curation · 2023Article
- An opinion mining methodology to analyse games for health.Multimedia tools and applications · 2023Article
- An Assessment of Mentions of Adverse Drug Events on Social Media With Natural Language Processing: Model Development and Analysis.JMIR medical informatics · 2022Article
- Challenges and opportunities for mining adverse drug reactions: perspectives from pharma, regulatory agencies, healthcare providers and consumers.Database : the journal of biological databases and curation · 2022Article
- Task reformulation and data-centric approach for Twitter medication name extraction.Database : the journal of biological databases and curation · 2022Article
- ADE Eval: An Evaluation of Text Processing Systems for Adverse Event Extraction from Drug Labels for Pharmacovigilance.Drug safety · 2021Article
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Abstract
objectiveThe 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 and machine learning challenges inherent to utilizing social media data for health informatics. In this paper, we present the annotated corpora, a technical summary of participants' systems, and the performance results.
methodsThe eighth iteration of the #SMM4H shared tasks was hosted at the AMIA 2023 Annual Symposium and consisted of 5 tasks that represented various social media platforms (Twitter and Reddit), languages (English and Spanish), methods (binary classification, multi-class classification, extraction, and normalization), and topics (COVID-19, therapies, social anxiety disorder, and adverse drug events).
resultsIn total, 29 teams registered, representing 17 countries. In general, the top-performing systems used deep neural network architectures based on pre-trained transformer models. In particular, the top-performing systems for the classification tasks were based on single models that were pre-trained on social media corpora.
conclusionTo facilitate future work, the datasets-a total of 61 353 posts-will remain available by request, and the CodaLab sites will remain active for a post-evaluation phase.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.