通过长期短期记忆模型检测药物不良反应信号
Mengqi Cao1, Yanna Chi2, Jinyang Yu3
1Department of Biostatistics, School of Public Health, Peking University, Beijing, China.
Frontiers in pharmacology
|July 8, 2025
概括
深度学习模型,特别是长期短期记忆 (LSTM),显著提高了药物不良反应 (ADR) 信号检测的准确性. 与药物安全监测的传统统计方法相比,这种先进的方法提供了更高的灵敏度和F1分数.
科学领域:
- 药物监督 药物监督 药物监督
- 医疗保健中的人工智能
- 计算生物学 计算生物学
背景情况:
- 药物安全是一个关键的公共卫生问题,传统的检测方法往往产生高错误阳性率.
- 有效的药物不良反应 (ADR) 信号检测对于患者安全和医疗保健经济至关重要.
研究的目的:
- 引入和评估用于ADR信号检测的深度学习模型.
- 为了比较长期短期记忆 (LSTM) 模型与传统统计方法的性能.
主要方法:
- 利用来自广东ADR监测中心的不良事件数据.
- 应用长短期记忆 (LSTM) 模型对自由文本数据进行分类预测.
- 将LSTM性能与后勤回归,随机森林,K-最近邻居和多层感知器模型进行比较.
主要成果:
- 在自由文本数据上,LSTM模型获得了95.16%的灵敏度和0.9706的F1得分.
- 传统的物流回归实现了86.83%的灵敏度和0.9063的F1得分.
- 深度学习模型在ADR信号检测的灵敏度和F1得分方面表现出卓越的表现.
结论:
- 深度学习模型显示了提高ADR监控性能的巨大潜力.
- 诸如药物原因,ADR情景和剂量等变量会影响ADR检测结果.
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