ArticleJournal of the American Medical Informatics Association : JAMIA2018
Data and systems for medication-related text classification and concept normalization from Twitter: insights from the Social Media Mining for Health (SMM4H)-2017 shared task.
Article in Journal of the American Medical Informatics Association : JAMIA, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 39 papers.
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Who cites it
39 citing papers in PubMed.
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- Rapid Screening of Anticoagulation Compounds for Biological Target-Associated Adverse Effects Using a Deep-Learning Framework in the Management of Atrial Fibrillation.Bioengineering (Basel, Switzerland) · 2025Article
- Position-context additive transformer-based model for classifying text data on social media.Scientific reports · 2025Article
- Performance and Reproducibility of Large Language Models in Named Entity Recognition: Considerations for the Use in Controlled Environments.Drug safety · 2025Article
- Overview of the 8th Social Media Mining for Health Applications (#SMM4H) shared tasks at the AMIA 2023 Annual Symposium.Journal of the American Medical Informatics Association : JAMIA · 2024Article
- Overview of the 8medRxiv : the preprint server for health sciences · 2023Article
- The Role of Social Media for Identifying Adverse Drug Events Data in Pharmacovigilance: Protocol for a Scoping Review.JMIR research protocols · 2023Article
- Article
- An overview of biomedical entity linking throughout the years.Journal of biomedical informatics · 2023Review
- Edinburgh_UCL_Health@SMM4H'22: From Glove to Flair for handling imbalanced healthcare corpora related to Adverse Drug Events, Change in medication and self-reporting vaccination.Proceedings of COLING. International Conference on Computational Linguistics · 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
- Comparison of Pretraining Models and Strategies for Health-Related Social Media Text Classification.Healthcare (Basel, Switzerland) · 2022Article
- A simple neural vector space model for medical concept normalization using concept embeddings.Journal of biomedical informatics · 2022Article
- Artificial Intelligence-Based Pharmacovigilance in the Setting of Limited Resources.Drug safety · 2022Review
- Machine Learning in Causal Inference: Application in Pharmacovigilance.Drug safety · 2022Review
- Detecting Personal Medication Intake in Twitter via Domain Attention-Based RNN with Multi-Level Features.Computational intelligence and neuroscience · 2022Article
- Tracking the COVID-19 outbreak in India through Twitter: Opportunities for social media based global pandemic surveillance.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2022Article
- Automatic Expansion of Domain-Specific Affective Models for Web Intelligence Applications.Cognitive computation · 2022Article
- DeepADEMiner: a deep learning pharmacovigilance pipeline for extraction and normalization of adverse drug event mentions on Twitter.Journal of the American Medical Informatics Association : JAMIA · 2021Article
- Pulse of the pandemic: Iterative topic filtering for clinical information extraction from social media.Journal of biomedical informatics · 2021Article
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16 authors.
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Abstract
Objective: We executed the Social Media Mining for Health (SMM4H) 2017 shared tasks to enable the community-driven development and large-scale evaluation of automatic text processing methods for the classification and normalization of health-related text from social media. An additional objective was to publicly release manually annotated data. Materials and Methods: We organized 3 independent subtasks: automatic classification of self-reports of 1) adverse drug reactions (ADRs) and 2) medication consumption, from medication-mentioning tweets, and 3) normalization of ADR expressions. Training data consisted of 15 717 annotated tweets for (1), 10 260 for (2), and 6650 ADR phrases and identifiers for (3); and exhibited typical properties of social-media-based health-related texts. Systems were evaluated using 9961, 7513, and 2500 instances for the 3 subtasks, respectively. We evaluated performances of classes of methods and ensembles of system combinations following the shared tasks. Results: Among 55 system runs, the best system scores for the 3 subtasks were 0.435 (ADR class F1-score) for subtask-1, 0.693 (micro-averaged F1-score over two classes) for subtask-2, and 88.5% (accuracy) for subtask-3. Ensembles of system combinations obtained best scores of 0.476, 0.702, and 88.7%, outperforming individual systems. Discussion: Among individual systems, support vector machines and convolutional neural networks showed high performance. Performance gains achieved by ensembles of system combinations suggest that such strategies may be suitable for operational systems relying on difficult text classification tasks (eg, subtask-1). Conclusions: Data imbalance and lack of context remain challenges for natural language processing of social media text. Annotated data from the shared task have been made available as reference standards for future studies (http://dx.doi.org/10.17632/rxwfb3tysd.1).
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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.