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

ABSTRACT:

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 6

SUBMITTER: Watanabe T 

PROVIDER: S-EPMC10850196 | biostudies-literature | 2024 Mar

REPOSITORIES: biostudies-literature