通过等级网络和患者访问历史记录增强药物推
概括
这项研究引入了使用患者病史的层次化药物推系统,以改善药物选择. 该模型提高了个性化处方的精度,以获得更好的患者护理结果.
科学领域:
- 医疗信息学 医疗信息学
- 医疗保健中的人工智能
- 药理学 药理学是指药理学的学科.
背景情况:
- 药物处方需要精确和个性化,以获得有效的患者护理.
- 现有的系统可能缺乏根据个体患者病史个性化推的能力.
- 药物分类的等级性 (例如,解剖治疗化学系统) 对推引擎构成了挑战.
研究的目的:
- 开发和评估一个层次化的药物推系统.
- 为了提高药物处方的准确性和个性化.
- 为了利用患者访问历史记录来增强药物选择.
主要方法:
- 使用快速文本嵌入医疗背景.
- 采用基于注意力的层次模型来管理药物代码层次结构.
- 结合了当前和前三次患者访问的数据.
- 在99,417次匿名初级保健门诊访问中训练并评估了该模型.
主要成果:
- 在解剖治疗化学 (ATC) 系统的多个级别中实现了高平均精度 (平均AP):0.8724 (一级),0.7419 (二级),0.6805 (三级) 和0.6184 (四级).
- 证明将患者访问历史记录纳入预测的准确性显著提高.
- 层次方法有效地处理药物数据的结构性.
结论:
- 开发的等级化药物推系统为改善药物处方提供了可行的解决方案.
- 整合患者历史数据可以提高药物推的个性化和准确性.
- 未来的工作可以探索扩展更全面和细微的药物建议.
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