摘要：

通过对电子病历中的用药数据和患者临床体征进行深度挖掘, 利用深度学习模型预测药物组合, 以期提高临床疾病诊疗过程中用药推荐的准确性和安全性。提出一种融合患者临床体征的图增强注意力药物推荐模型, 首先融入患者临床体征来丰富患者特征表示, 并利用图神经网络(GNN)获取组合用药和药物相互作用(DDI)知识, 通过两阶段注意力模块获取融合了患者历史用药信息和DDI知识的全新患者表示, 最后以多标签学习方式进行药物推荐。在MIMIC-Ⅲ公开数据集上进行实验验证, 此模型Jaccard相似度、精确率-召回率曲线下面积(PR-AUC)、F1值及DDI率分别达到0.517 2、0.766 1、0.673 1、0.070 3, 相较于最近的主流药物推荐模型, 其DDI率降低了0.004 7以上, Jaccard相似度、PR-AUC、F1值分别提升了0.004 5、0.006 1和0.012 1以上; 为了进一步验证此模型的性能, 在真实世界数据集上也做了对比实验, 结果表明此模型Jaccard相似度、PR-AUC、F1值及DDI率分别达到0.450 2、0.702 3、0.612 8、0.085 7, 均优于最近的主流药物推荐模型。以上实验结果表明此模型具有较好的性能和临床适用性, 对辅助医生制定更加科学有效的用药方案具有重要的现实意义。

关键词: 电子病历, 临床体征, 药物推荐, 图神经网络, 两阶段注意力机制, 患者特征嵌入表示

Abstract:

By deeply mining medication data and clinical features of patients from electronic medical records, this study leverages deep learning models to predict drug combinations. This study aims to enhance the accuracy and safety of medication recommendations in clinical disease management and proposes a graph-enhanced attention drug recommendation model that integrates the clinical features of patients to enrich patient representations. This model uses Graph Neural Network (GNN) to capture drug combination and Drug-Drug Interaction (DDI) knowledge. Through a two-stage attention mechanism, the model generates novel patient representations that combine historical medication information with DDI knowledge. Finally, a multi-label learning approach is employed for drug recommendation. Experiments on the MIMIC-Ⅲ public dataset demonstrate that the proposed model achieves a Jaccard similarity, Precise—Recall Area Under the Curve (PR-AUC), F1 value, and DDI rate of 0.517 2, 0.766 1, 0.673 1, and 0.070 3, respectively. Compared to recent state-of-the-art drug recommendation models, the proposed model reduces the DDI rate by at least 0.004 7, and it improves Jaccard similarity, PR-AUC, and F1 value by 0.004 5、0.006 1 and 0.012 1 or more, respectively. Comparative experiments on real-world datasets further validate the model's performance. The model outperforms recent state-of-the-art drug recommendation models with a Jaccard similarity, PR-AUC, F1 value, and DDI rate of 0.450 2, 0.702 3, 0.612 8, and 0.085 7, respectively. These experimental results indicate that the proposed model exhibits superior performance and clinical applicability, providing significant value in assisting physicians in developing more scientifically effective medication plans.

Key words: electronic medical record, clinical features, drug recommendation, Graph Neural Network (GNN), two-stage attention mechanism, patient characteristics embedded representation