Abstract：
General practitioners are supposed to be better diagnostics to detect patients with serious diseases earlier, and conduct early interventions and appropriate referrals of patients. However, in the current general practice, primary general practitioners lack sufficient clinical experiences, and the correct rate of general disease diagnosis is low. To assist general practitioners in diagnosis, this paper proposes a multi-label hierarchical classification method based on graph neural network, which integrates medical knowledge and electronic health record (EHR) data to build a disease prediction model. The experimental results based on data consist of 231,783 visits from EHR show that the proposed model outperforms all baseline models in the general disease prediction task with a top-3 recall of 0.865. The interpretable results of the model can effectively help clinicians understand the basis of the model\'s decision-making.
摘要：
全科医生应该是更好的诊断方法，以便更早地发现患有严重疾病的患者，并对患者进行早期干预和适当转诊。然而,在当前的一般实践中，初级全科医生缺乏足够的临床经验，一般疾病诊断正确率低。协助全科医生诊断，提出了一种基于图神经网络的多标签层次分类方法,整合医学知识和电子健康记录(EHR)数据,构建疾病预测模型。基于来自EHR的231,783次访问的数据的实验结果表明，所提出的模型在一般疾病预测任务中优于所有基线模型，前3名召回率为0.865。模型的可解释结果可以有效帮助临床医生了解模型的决策依据。
