ReviewPharmaceutics2022
Using Machine Learning for Pharmacovigilance: A Systematic Review.
Review in Pharmaceutics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 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
17 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Pooled it
- Reviewing the Computational Landscape of Drug Repurposing: Evolution from Structure-Based Methods to LLM-Based Methods.Biomolecules · 2026Review
- Predicting Serious Adverse Events, Medication Abuse, Misuse, and Risk of Dependence for Medications with High Dependence Potential: Role of Patient-Reported Factors and Machine Learning Approach.Healthcare (Basel, Switzerland) · 2026Article
- Commentary on the LMSz method: a novel approach to gene-specific growth charts for rare neurodevelopmental disorders.European journal of human genetics : EJHG · 2026Article
- Design Principles for Interactive Dashboards in Drug Safety Surveillance: Design Science Research.JMIR medical informatics · 2026Article
- Article
- Risk assessment of drug-associated miscarriage using XGBoost and SHAP explainability: a real-world pharmacovigilance analysis based on the FAERS database.Frontiers in pharmacology · 2026Article
- Artificial intelligence in pharmacovigilance: a narrative review and practical experience with an expert-defined Bayesian network tool.International journal of clinical pharmacy · 2025Review
- Pharmacovigilance in the Era of Artificial Intelligence: Advancements, Challenges, and Considerations.Cureus · 2025Review
- Leveraging Natural Language Processing and Machine Learning Methods for Adverse Drug Event Detection in Electronic Health/Medical Records: A Scoping Review.Drug safety · 2025Article
- Safety assessment of ripretinib: a real-world adverse event analysis from the food and drug administration adverse event reporting system.Frontiers in oncology · 2025Article
- Predicting patients' sentiments about medications using artificial intelligence techniques.Scientific reports · 2024Article
- Enhancing the coverage of SemRep using a relation classification approach.Journal of biomedical informatics · 2024Article
- Pharmacovigilance in Vaccines: Importance, Main Aspects, Perspectives, and Challenges-A Narrative Review.Pharmaceuticals (Basel, Switzerland) · 2024Review
- A discovery and verification approach to pharmacovigilance using electronic healthcare data.Frontiers in pharmacology · 2024Article
- Pharmacovigilance-based drug repurposing: searching for putative drugs with hypohidrosis or anhidrosis adverse events for use against hyperhidrosis.European journal of medical research · 2023Observational
- AI-Based Computational Methods in Early Drug Discovery and Post Market Drug Assessment: A Survey.IEEE transactions on computational biology and bioinformaticsReview
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Pharmacovigilance is a science that involves the ongoing monitoring of adverse drug reactions to existing medicines. Traditional approaches in this field can be expensive and time-consuming. The application of natural language processing (NLP) to analyze user-generated content is hypothesized as an effective supplemental source of evidence. In this systematic review, a broad and multi-disciplinary literature search was conducted involving four databases. A total of 5318 publications were initially found. Studies were considered relevant if they reported on the application of NLP to understand user-generated text for pharmacovigilance. A total of 16 relevant publications were included in this systematic review. All studies were evaluated to have medium reliability and validity. For all types of drugs, 14 publications reported positive findings with respect to the identification of adverse drug reactions, providing consistent evidence that natural language processing can be used effectively and accurately on user-generated textual content that was published to the Internet to identify adverse drug reactions for the purpose of pharmacovigilance. The evidence presented in this review suggest that the analysis of textual data has the potential to complement the traditional system of pharmacovigilance.
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.