[基于知识图的潜在不合适药物的预测]
Gongchao Lin1, Fei Teng1, Qiaozhi Hu2
1School of Computing and Artificial Intelligence, Southwest Jiaotong University, Chengdu 611756, China.
本研究引入了一种结合知识图和机器学习的新型模型,以提高潜在不适当药物 (PIM) 预测的准确性. 新方法改善了低频PIM的识别,从而提高了患者的安全性.
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
- 临床决策支持 临床决策支持
背景情况:
- 潜在不合适的药物 (PIM) 使用对患者的安全构成重大风险,特别是在脆弱人群中.
- 现有的PIM预测模型经常在准确性和识别不太常见的PIM方面扎.
研究的目的:
- 开发和评估一个新的PIM预测模型,整合知识图和机器学习.
- 提高PIM预测的准确性和可靠性,特别是对于低频PIM.
主要方法:
- 使用2019年的Beers标准和知识图构建了一个PIM知识表示框架.
- 实施了从患者数据到PIM节点的PIM推断过程.
- 使用分类器链算法开发了一种机器学习预测模型,将低频PIM的知识图推理结果纳入其中.
主要成果:
- 该模型实现了PIM数量预测的98.10%准确率和93.66%的F1得分.
- 在PIM多标签预测中显示了0.06%的哈明损失和66.09%的宏F1.
- 在预测准确度方面表现优于现有模型,特别是对于低频PIM标签.
结论:
- 拟议的知识图和机器学习综合模型显著提高了PIM预测性能.
- 该方法有效地提高了低频PIM标签的识别,有助于更安全的药物实践.
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