ArticleJournal of the American Medical Informatics Association : JAMIA
Text mining for the Vaccine Adverse Event Reporting System: medical text classification using informative feature selection.
Article in Journal of the American Medical Informatics Association : JAMIA. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 45 papers.
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Who cites it
45 citing papers in PubMed.
- Preparing Allergists to Practice in 2050 Using Artificial Intelligence.The journal of allergy and clinical immunology. In practice · 2025Review
- Artificial intelligence in pharmacovigilance: a narrative review and practical experience with an expert-defined Bayesian network tool.International journal of clinical pharmacy · 2025Review
- Computational tools and data integration to accelerate vaccine development: challenges, opportunities, and future directions.Frontiers in immunology · 2025Review
- The Impact of Artificial Intelligence on Allergy Diagnosis and Treatment.Current allergy and asthma reports · 2024Review
- Trust but Verify: Lessons Learned for the Application of AI to Case-Based Clinical Decision-Making From Postmarketing Drug Safety Assessment at the US Food and Drug Administration.Journal of medical Internet research · 2024Article
- Improving long COVID-related text classification: a novel end-to-end domain-adaptive paraphrasing framework.Scientific reports · 2024Article
- Article
- Generalizability of machine learning methods in detecting adverse drug events from clinical narratives in electronic medical records.Frontiers in pharmacology · 2023Article
- Usefulness of Vaccine Adverse Event Reporting System for Machine-Learning Based Vaccine Research: A Case Study for COVID-19 Vaccines.International journal of molecular sciences · 2022Article
- "Artificial Intelligence" for Pharmacovigilance: Ready for Prime Time?Drug safety · 2022Article
- When BERT meets Bilbo: a learning curve analysis of pretrained language model on disease classification.BMC medical informatics and decision making · 2022Article
- A scholarly network of AI research with an information science focus: Global North and Global South perspectives.PloS one · 2022Article
- Text Mining of Adverse Events in Clinical Trials: Deep Learning Approach.JMIR medical informatics · 2021Article
- Artificial Intelligence for Identifying the Prevention of Medication Incidents Causing Serious or Moderate Harm: An Analysis Using Incident Reporters' Views.International journal of environmental research and public health · 2021Article
- Machine Learning Approach for Active Vaccine Safety Monitoring.Journal of Korean medical science · 2021Article
- Extracting postmarketing adverse events from safety reports in the vaccine adverse event reporting system (VAERS) using deep learning.Journal of the American Medical Informatics Association : JAMIA · 2021Article
- Article
- Automatic Annotation of Narrative Radiology Reports.Diagnostics (Basel, Switzerland) · 2020Article
- The use of natural language processing to identify vaccine-related anaphylaxis at five health care systems in the Vaccine Safety Datalink.Pharmacoepidemiology and drug safety · 2020Article
- Innovation in Pharmacovigilance: Use of Artificial Intelligence in Adverse Event Case Processing.Clinical pharmacology and therapeutics · 2019Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveThe US Vaccine Adverse Event Reporting System (VAERS) collects spontaneous reports of adverse events following vaccination. Medical officers review the reports and often apply standardized case definitions, such as those developed by the Brighton Collaboration. Our objective was to demonstrate a multi-level text mining approach for automated text classification of VAERS reports that could potentially reduce human workload.
designWe selected 6034 VAERS reports for H1N1 vaccine that were classified by medical officers as potentially positive (N(pos)=237) or negative for anaphylaxis. We created a categorized corpus of text files that included the class label and the symptom text field of each report. A validation set of 1100 labeled text files was also used. Text mining techniques were applied to extract three feature sets for important keywords, low- and high-level patterns. A rule-based classifier processed the high-level feature representation, while several machine learning classifiers were trained for the remaining two feature representations. MEASUREMENTS: Classifiers' performance was evaluated by macro-averaging recall, precision, and F-measure, and Friedman's test; misclassification error rate analysis was also performed.
resultsRule-based classifier, boosted trees, and weighted support vector machines performed well in terms of macro-recall, however at the expense of a higher mean misclassification error rate. The rule-based classifier performed very well in terms of average sensitivity and specificity (79.05% and 94.80%, respectively).
conclusionOur validated results showed the possibility of developing effective medical text classifiers for VAERS reports by combining text mining with informative feature selection; this strategy has the potential to reduce reviewer workload considerably.
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Registered trials
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.