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タイトル: | Early Detection of Adverse Drug Reaction Signals by Association Rule Mining Using Large-Scale Administrative Claims Data |
著者: | Yamamoto, Hiroki Kayanuma, Gen Nagashima, Takuya Toda, Chihiro Nagayasu, Kazuki ![]() ![]() Kaneko, Shuji ![]() ![]() ![]() |
著者名の別形: | 山本, 浩貴 栢沼, 玄 長島, 卓也 戸田, 千尋 永安, 一樹 金子, 周司 |
発行日: | Apr-2023 |
出版者: | Springer Nature |
誌名: | Drug Safety |
巻: | 46 |
号: | 4 |
開始ページ: | 371 |
終了ページ: | 389 |
抄録: | INTRODUCTION: Adverse drug reactions (ADRs) are a leading cause of mortality worldwide and should be detected promptly to reduce health risks to patients. A data-mining approach using large-scale medical records might be a useful method for the early detection of ADRs. Many studies have analyzed medical records to detect ADRs; however, most of them have focused on a narrow range of ADRs, limiting their usefulness. OBJECTIVE: This study aimed to identify methods for the early detection of a wide range of ADR signals. METHODS: First, to evaluate the performance in signal detection of ADRs by data-mining, we attempted to create a gold standard based on clinical evidence. Second, association rule mining (ARM) was applied to patient symptoms and medications registered in claims data, followed by evaluating ADR signal detection performance. RESULTS: We created a new gold standard consisting of 92 positive and 88 negative controls. In the assessment of ARM using claims data, the areas under the receiver-operating characteristic curve and the precision-recall curve were 0.80 and 0.83, respectively. If the detection criteria were defined as lift > 1, conviction > 1, and p-value < 0.05, ARM could identify 156 signals, of which 90 were true positive controls (sensitivity: 0.98, specificity: 0.25). Evaluation of the capability of ARM with short periods of data revealed that ARM could detect a greater number of positive controls than the conventional analysis method. CONCLUSIONS: ARM of claims data may be effective in the early detection of a wide range of ADR signals. |
著作権等: | © The Author(s) 2023 This article is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License, which permits any non-commercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. |
URI: | http://hdl.handle.net/2433/284466 |
DOI(出版社版): | 10.1007/s40264-023-01278-4 |
PubMed ID: | 36828947 |
出現コレクション: | 学術雑誌掲載論文等 |

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