Predicting the Addition of Information Regarding Clinically Significant Adverse Drug Reactions to Japanese Drug Package Inserts Using a Machine-Learning Model.

Purpose: To 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. Methods: We 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). Results: We 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." Conclusion: Our 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. [ABSTRACT FROM AUTHOR]