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开发一种基于人工智能的预测模型,用于注射药物的过敏性休克,使用日本的真实世界数据和基于化学结构的分析
Tomoyuki Enokiya1, Kaito Ozaki2
1Laboratory of Pharmacoinformatics, Department of Pharmaceutical Sciences, Faculty of Pharmaceutical Sciences, Suzuka University of Medical Science, 3500-3 Minamitamagakichō, Suzuka, Mie Prefecture, 513-8670, Japan. tenokiya@suzuka-u.ac.jp.
概括
一个人工智能模型使用日本真实世界的数据和化学结构预测注射药物引起过敏性休克. 这个工具有助于药物安全性评估和评估过敏性休克风险.
科学领域:
- 药物监督 药物监督 药物监督
- 人工智能在药物安全方面的作用
- 计算化学的计算化学
背景情况:
- 过敏性休克是一种严重的不良药物反应.
- 预测诱导过敏性休克的药物对于患者的安全至关重要.
研究的目的:
- 开发一种基于人工智能的预测模型,用于注射药物引起过敏性休克.
- 使用日本真实世界数据 (JADER数据库) 和化学结构分析.
主要方法:
- 从JADER (2004年4月至2020年12月) 提取的不良药物反应数据.
- 选择的药物具有"过敏性休克"作为不良反应.
- 采用后勤回归,LASSO,XGBoost,RF,SVM和NNW模型,使用化学特性和结构相似性.
- 应用主要组件分析以减少尺寸和交叉验证以优化.
主要成果:
- 分析了227种与过敏性休克相关的注射药物.
- 使用AUC,灵敏度和特异性评估模型性能.
- 确定了mepivacaine的结构相似性作为最终组合模型中的关键预测因素.
结论:
- 成功开发了一个人工智能模型来预测诱导过敏性休克的注射药物.
- 该模型显示了提高药物安全性评估和过敏性休克风险评估的潜力.
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