| アブストラクト | The addition of clinically significant adverse reactions (CSARs) to Japanese package inserts (PIs) is an important safety measure that can be used to inform medical personnel of potential health risks; however, determining the necessity of their addition can be lengthy and complex. Therefore, we aimed to construct a machine learning-based model that can predict the addition of CSARs at an early stage due to the accumulation of both Japanese and overseas adverse drug reaction (ADR) cases. The target comprised CSARs added to PIs from August 2011 to March 2022. The control group consisted of drugs without the same CSARs in their PIs by March 2022. Features were generated using ADR case accumulation data obtained from the Japanese Adverse Drug Event Report and the U.S. Food and Drug Administration Adverse Event Reporting System databases. The model was constructed using DataRobot, and its performance evaluated using the Matthews correlation coefficient. The target for the addition of CSARs included 414 cases, comprising 302 due to domestic case accumulation, 22 due to both domestic and overseas case accumulation, 12 due to overseas case accumulation, and 78 due to revisions of the company core data sheet. The best model was a generalized linear model with informative features, achieving a cross-validation of 0.8754 and a holdout of 0.8995. In conclusion, the proposed model effectively predicted CSAR additions to PIs resulting from the accumulation of ADR cases using data from both Japan and the United States. |
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| ジャーナル名 | Biological & pharmaceutical bulletin |
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| Pubmed追加日 | 2024/3/14 |
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| 投稿者 | Watanabe, Takashi; Ambe, Kaori; Tohkin, Masahiro |
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| 組織名 | Department of Regulatory Science, Graduate School of Pharmaceutical Sciences,;Nagoya City University. |
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| Pubmed リンク | https://www.ncbi.nlm.nih.gov/pubmed/38479885/ |
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