ArticleBMC medical informatics and decision making2014
A pipeline to extract drug-adverse event pairs from multiple data sources.
Article in BMC medical informatics and decision making, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
23 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Systematic review on the prevalence, frequency and comparative value of adverse events data in social media.British journal of clinical pharmacology · 2015Pooled it
- Monitoring Adverse Drug Events in Web Forums: Evaluation of a Pipeline and Use Case Study.Journal of medical Internet research · 2024Article
- Drug_SNSMiner: standard pharmacovigilance pipeline for detection of adverse drug reaction using SNS data.Scientific reports · 2023Article
- An industry perspective on the use of machine learning in drug and vaccine safety.Frontiers in drug safety and regulation · 2023Article
- LiSA: an assisted literature search pipeline for detecting serious adverse drug events with deep learning.BMC medical informatics and decision making · 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
- Discovering symptom patterns of COVID-19 patients using association rule mining.Computers in biology and medicine · 2021Article
- Detecting and Filtering Immune-Related Adverse Events Signal Based on Text Mining and Observational Health Data Sciences and Informatics Common Data Model: Framework Development Study.JMIR medical informatics · 2020Article
- An ensemble of neural models for nested adverse drug events and medication extraction with subwords.Journal of the American Medical Informatics Association : JAMIA · 2020Article
- The Adverse Drug Reactions From Patient Reports in Social Media Project: Protocol for an Evaluation Against a Gold Standard.JMIR research protocols · 2019Article
- Adverse Drug Reaction Related Post Detecting Using Sentiment Feature.Iranian journal of public health · 2018Article
- Mining Patients' Narratives in Social Media for Pharmacovigilance: Adverse Effects and Misuse of Methylphenidate.Frontiers in pharmacology · 2018Article
- Toward multimodal signal detection of adverse drug reactions.Journal of biomedical informatics · 2017Article
- 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
- Filtering Entities to Optimize Identification of Adverse Drug Reaction From Social Media: How Can the Number of Words Between Entities in the Messages Help?JMIR public health and surveillance · 2017Article
- The SIDER database of drugs and side effects.Nucleic acids research · 2016Article
- Extraction of a group-pair relation: problem-solving relation from web-board documents.SpringerPlus · 2016Article
- Social media and pharmacovigilance: A review of the opportunities and challenges.British journal of clinical pharmacology · 2015Review
- Leveraging MEDLINE indexing for pharmacovigilance - Inherent limitations and mitigation strategies.Journal of biomedical informatics · 2015Article
- Adverse Drug Reaction Identification and Extraction in Social Media: A Scoping Review.Journal of medical Internet research · 2015Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundPharmacovigilance aims to uncover and understand harmful side-effects of drugs, termed adverse events (AEs). Although the current process of pharmacovigilance is very systematic, the increasing amount of information available in specialized health-related websites as well as the exponential growth in medical literature presents a unique opportunity to supplement traditional adverse event gathering mechanisms with new-age ones.
methodWe present a semi-automated pipeline to extract associations between drugs and side effects from traditional structured adverse event databases, enhanced by potential drug-adverse event pairs mined from user-comments from health-related websites and MEDLINE abstracts. The pipeline was tested using a set of 12 drugs representative of two previous studies of adverse event extraction from health-related websites and MEDLINE abstracts.
resultsTesting the pipeline shows that mining non-traditional sources helps substantiate the adverse event databases. The non-traditional sources not only contain the known AEs, but also suggest some unreported AEs for drugs which can then be analyzed further.
conclusionA semi-automated pipeline to extract the AE pairs from adverse event databases as well as potential AE pairs from non-traditional sources such as text from MEDLINE abstracts and user-comments from health-related websites is presented.
Indexed as
Identifiers
What OpenQuestion holds
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.