School of Computer Science and Engineering, Vellore Institute of Technology - Chennai, Chennai, India.
Centre for Advanced Data Science, Vellore Institute of Technology - Chennai, Chennai, India.
Sci Rep. 2024 Oct 26;14(1):25449. doi: 10.1038/s41598-024-75784-5.
Medicine recommendation systems are designed to aid healthcare professionals by analysing a patient's admission data to recommend safe and effective medications. These systems are categorised into two types: instance-based and longitudinal-based. Instance-based models only consider the current admission, while longitudinal models consider the patient's medical history. Electronic Health Records are used to incorporate medical history into longitudinal models. This project proposes a novel Knowledge Graph-Driven Medicine Recommendation System using Graph Neural Networks, KGDNet, that utilises longitudinal EHR data along with ontologies and Drug-Drug Interaction knowledge to construct admission-wise clinical and medicine Knowledge Graphs for every patient. Recurrent Neural Networks are employed to model a patient's historical data, and Graph Neural Networks are used to learn embeddings from the Knowledge Graphs. A Transformer-based Attention mechanism is then used to generate medication recommendations for the patient, considering their current clinical state, medication history, and joint medical records. The model is evaluated on the MIMIC-IV EHR data and outperforms existing methods in terms of precision, recall, F1 score, Jaccard score, and Drug-Drug Interaction control. An ablation study on our models various inputs and components to provide evidence for the importance of each component in providing the best performance. Case study is also performed to demonstrate the real-world effectiveness of KGDNet.
医学推荐系统旨在通过分析患者的入院数据来为医疗保健专业人员提供帮助，为其推荐安全有效的药物。这些系统分为两类：基于实例的和基于纵向的。基于实例的模型仅考虑当前的入院情况，而基于纵向的模型则考虑患者的病史。电子健康记录用于将病史纳入纵向模型中。本项目提出了一种新颖的基于知识图和图神经网络的医学推荐系统，即 KGDNet，该系统使用图神经网络，利用纵向 EHR 数据以及本体和药物-药物相互作用知识，为每个患者构建基于入院的临床和药物知识图。递归神经网络用于对患者的历史数据进行建模，图神经网络用于从知识图中学习嵌入。然后，使用基于转换器的注意力机制根据患者当前的临床状态、用药史和联合病历为其生成用药建议。该模型在 MIMIC-IV EHR 数据上进行了评估，在精度、召回率、F1 得分、Jaccard 得分和药物-药物相互作用控制方面均优于现有方法。我们对模型的各种输入和组件进行了消融研究，为每个组件在提供最佳性能方面的重要性提供了证据。还进行了案例研究，以证明 KGDNet 在实际应用中的有效性。
