Abstract：
Heart failure has become a huge public health problem, and failure to accurately predict readmission will further lead to the disease\'s high cost and high mortality. The construction of readmission prediction model can assist doctors in making decisions to prevent patients from deteriorating and reduce the cost burden. This paper extracts the patient discharge records from the MIMIC-III database. It divides the patients into three research categories: no readmission, readmission within 30 days, and readmission after 30 days, to predict the readmission of patients. We propose the HR-BGCN model to predict the readmission of patients. First, we use the Adaptive-TMix to improve the prediction indicators of a few categories and reduce the impact of unbalanced categories. Then, the knowledge-informed graph attention mechanism is proposed. By introducing a document-level explicit diagram structure, the coding ability of graph node features is significantly improved. The paragraph-level representation obtained through graph learning is combined with the context token-level representation of BERT, and finally, the multi-classification task is carried out. We also compare several typical graph learning classification models to verify the model\'s effectiveness, such as the IA-GCN model, GAT model, etc. The results show that the average F1 score of the HR-BGCN model proposed in this paper for 30-day readmission of heart failure patients is 88.26%, and the average accuracy is 90.47%. The HR-BGCN model is significantly better than the graph learning classification model for predicting heart failure readmission. It can help doctors predict the 30-day readmission of patients, then reduce the readmission rate of patients.
摘要：
心力衰竭已经成为一个巨大的公共卫生问题，不能准确预测再入院将进一步导致疾病的高成本和高死亡率。构建再入院预测模型可以辅助医生进行决策,防止病患恶化,减轻费用负担。本文从MIMIC-III数据库中提取患者出院记录。它将患者分为三个研究类别：没有再入院，30天内重新接纳，30天后再入院，预测患者的再入院。我们提出了HR-BGCN模型来预测患者的再入院。首先,我们使用Adaptive-TMix来改进几个类别的预测指标，并减少不平衡类别的影响。然后，提出了基于知识的图注意机制。通过引入文档级显式图结构，图节点特征的编码能力显著提高。通过图学习获得的段落级表示与BERT的上下文令牌级表示相结合，最后，进行多分类任务。我们还比较了几种典型的图学习分类模型，以验证模型的有效性。例如IA-GCN模型，GAT模型,等。结果表明,本文提出的HR-BGCN模型对心力衰竭患者30天再入院的平均F1评分为88.26%,平均准确率为90.47%。HR-BGCN模型在预测心力衰竭再入院方面明显优于图学习分类模型。它可以帮助医生预测30天患者的再入院时间，然后降低患者的再入院率。
