ArticleTherapeutic innovation & regulatory science2024
Predicting the Addition of Information Regarding Clinically Significant Adverse Drug Reactions to Japanese Drug Package Inserts Using a Machine-Learning Model.
Article in Therapeutic innovation & regulatory science, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers, 2 of them syntheses that pooled it.
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Who cites it
4 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Machine learning methods for predicting adverse drug events: A systematic review.British journal of clinical pharmacology · 2026Pooled it
- Artificial intelligence and computational methods in the Asia-Pacific pharmacovigilance landscape: a systematic review.International journal of public health · 2026Pooled it
- Artificial Intelligence for Drug Safety Across the Lifecycle and Decision Type: A Scoping Review.Pharmaceuticals (Basel, Switzerland) · 2026Review
- Predicting tigecycline-related adverse events in infected patients: a machine learning approach with clinical interpretability.Frontiers in pharmacology · 2025Article
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Authors and funding
3 authors.
Funding
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Abstract
purposeTo develop a machine learning (ML)-based model for predicting the addition of clinically significant adverse reaction (CSAR)-associated information to drug package inserts (PIs) based on information of adverse drug reaction (ADR) cases during the post-marketing stage in Japan.
methodsWe collected data on CSARs added to PIs from August 2011 to March 2020. ADR cases that led to CSARs resulting in PI revisions were considered as a positive case, and ML was used to construct a binary classification model to predict the PI revisions. We selected 34 features based on the ADR aggregate data collected 6 months before PI revisions. Prediction performance was evaluated using the Matthews correlation coefficient (MCC).
resultsWe found CSAR information added to PIs in 617 cases, 334 of which were due to the accumulation of domestic cases, and used only domestic case data for the prediction model. Among prediction models developed using several kinds of algorithms, the support vector machine with the radial basis function kernel with feature selection showed the highest predictive performance, having an MCC of 0.938 for the cross-validation and 0.922 for the test dataset. The feature with the highest importance in the model was the "average number of patients reported per quarter."
conclusionOur model accurately predicted PI revisions using information on ADR cases that occurred 6 months before. This is the first ML model that can predict the necessary safety measures and is an efficient method for guiding the decision to adopt additional safety measures early.
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