ArticleJMIR public health and surveillance2017
Filtering Entities to Optimize Identification of Adverse Drug Reaction From Social Media: How Can the Number of Words Between Entities in the Messages Help?
Article in JMIR public health and surveillance, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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Who cites it
17 citing papers in PubMed, 36 citations in OpenAlex.
- Discussions of Antibiotic Resistance on Social Media Platforms: Text Mining and Mixed Methods Content Analysis Study.JMIR formative research · 2025Article
- Monitoring Adverse Drug Events in Web Forums: Evaluation of a Pipeline and Use Case Study.Journal of medical Internet research · 2024Article
- Assessment of the Early Detection of Anosmia and Ageusia Symptoms in COVID-19 on Twitter: Retrospective Study.JMIR infodemiology · 2023Article
- Social Media Listening and Digital Profiling Study of People With Headache and Migraine: Retrospective Infodemiology Study.Journal of medical Internet research · 2023Article
- Language-agnostic pharmacovigilant text mining to elicit side effects from clinical notes and hospital medication records.Basic & clinical pharmacology & toxicology · 2022Article
- A New Method to Extract Health-Related Quality of Life Data From Social Media Testimonies: Algorithm Development and Validation.Journal of medical Internet research · 2022Article
- Social Media Platforms Listening Study on Atopic Dermatitis: Quantitative and Qualitative Findings.Journal of medical Internet research · 2022Article
- Assessing Patient Perceptions and Experiences of Paracetamol in France: Infodemiology Study Using Social Media Data Mining.Journal of medical Internet research · 2021Article
- Mapping and Modeling of Discussions Related to Gastrointestinal Discomfort in French-Speaking Online Forums: Results of a 15-Year Retrospective Infodemiology Study.Journal of medical Internet research · 2020Article
- Ontological Organization and Bioinformatic Analysis of Adverse Drug Reactions From Package Inserts: Development and Usability Study.Journal of medical Internet research · 2020Article
- Infodemiology and Infoveillance: Scoping Review.Journal of medical Internet research · 2020Article
- Extraction of Information Related to Adverse Drug Events from Electronic Health Record Notes: Design of an End-to-End Model Based on Deep Learning.JMIR medical informatics · 2018Article
- Web-Based Signal Detection Using Medical Forums Data in France: Comparative Analysis.Journal of medical Internet research · 2018Article
- Clinical Relation Extraction Toward Drug Safety Surveillance Using Electronic Health Record Narratives: Classical Learning Versus Deep Learning.JMIR public health and surveillance · 2018Article
- Detection of Cases of Noncompliance to Drug Treatment in Patient Forum Posts: Topic Model Approach.Journal of medical Internet research · 2018Article
- The Adverse Drug Reactions from Patient Reports in Social Media Project: Five Major Challenges to Overcome to Operationalize Analysis and Efficiently Support Pharmacovigilance Process.JMIR research protocols · 2017Article
- Identifying Profiles and Symptoms of Patients With Long COVID in France: Data Mining Infodemiology Study Based on Social Media.JMIR infodemiologyArticle
Corrections and comments
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Authors and funding
4 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundWith the increasing popularity of Web 2.0 applications, social media has made it possible for individuals to post messages on adverse drug reactions. In such online conversations, patients discuss their symptoms, medical history, and diseases. These disorders may correspond to adverse drug reactions (ADRs) or any other medical condition. Therefore, methods must be developed to distinguish between false positives and true ADR declarations.
objectiveThe aim of this study was to investigate a method for filtering out disorder terms that did not correspond to adverse events by using the distance (as number of words) between the drug term and the disorder or symptom term in the post. We hypothesized that the shorter the distance between the disorder name and the drug, the higher the probability to be an ADR.
methodsWe analyzed a corpus of 648 messages corresponding to a total of 1654 (drug and disorder) pairs from 5 French forums using Gaussian mixture models and an expectation-maximization (EM) algorithm .
resultsThe distribution of the distances between the drug term and the disorder term enabled the filtering of 50.03% (733/1465) of the disorders that were not ADRs. Our filtering strategy achieved a precision of 95.8% and a recall of 50.0%.
conclusionsThis study suggests that such distance between terms can be used for identifying false positives, thereby improving ADR detection in social media.
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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.