ReviewDrug safety2022
Machine Learning in Causal Inference: Application in Pharmacovigilance.
Review in Drug safety, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 14 papers.
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
14 citing papers in PubMed.
- A tri-scale in silico framework integrating pharmacovigilance and mechanistic modeling suggests tepotinib-associated acute kidney injury risk.Renal failure · 2026Article
- Using Artificial Intelligence to Identify Patterns and Predictors of Adverse Drug Reactions in Cardiovascular Patients: A Comprehensive Narrative Review.American journal of cardiovascular drugs : drugs, devices, and other interventions · 2026Review
- AI for Causality Assessment in Pharmacovigilance: Protocol for a Scoping Review.JMIR research protocols · 2026Article
- Beyond black boxes: using explainable causal artificial intelligence to separate signal from noise in pharmacovigilance.International journal of clinical pharmacy · 2026Article
- Early detection of chronic kidney disease based on a SURD-enhanced machine learning model.Scientific reports · 2026Article
- A kNN based machine learning approach to automating causality assessment of adverse events.Scientific reports · 2026Article
- Artificial Intelligence in Recurrent Pregnancy Loss: Current Evidence, Limitations, and Future Directions.Journal of clinical medicine · 2026Review
- Waldo: Automated discovery of adverse events from unstructured self reports.PLOS digital health · 2025Article
- 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
- Research Advance of Causal Inference in Clinical Medicine: A Bibliometrics Analysis via Citespace.Journal of multidisciplinary healthcare · 2025Review
- Artificial intelligence in pharmacovigilance: advancing drug safety monitoring and regulatory integration.Therapeutic advances in drug safety · 2025Review
- Development and validation of a machine learning-based detection system to improve precision screening for medication errors in the neonatal intensive care unit.Frontiers in pharmacology · 2023Article
- Artificial Intelligence and Machine Learning for Safe Medicines.Drug safety · 2022Article
Corrections and comments
- Erratum issued
Authors and funding
7 authors.
Funding
Abstract
Monitoring adverse drug events or pharmacovigilance has been promoted by the World Health Organization to assure the safety of medicines through a timely and reliable information exchange regarding drug safety issues. We aim to discuss the application of machine learning methods as well as causal inference paradigms in pharmacovigilance. We first reviewed data sources for pharmacovigilance. Then, we examined traditional causal inference paradigms, their applications in pharmacovigilance, and how machine learning methods and causal inference paradigms were integrated to enhance the performance of traditional causal inference paradigms. Finally, we summarized issues with currently mainstream correlation-based machine learning models and how the machine learning community has tried to address these issues by incorporating causal inference paradigms. Our literature search revealed that most existing data sources and tasks for pharmacovigilance were not designed for causal inference. Additionally, pharmacovigilance was lagging in adopting machine learning-causal inference integrated models. We highlight several currently trending directions or gaps to integrate causal inference with machine learning in pharmacovigilance research. Finally, our literature search revealed that the adoption of causal paradigms can mitigate known issues with machine learning models. We foresee that the pharmacovigilance domain can benefit from the progress in the machine learning field.
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.