论文标题
通过知识图预测医学文本的患者再入院风险
Predicting Patient Readmission Risk from Medical Text via Knowledge Graph Enhanced Multiview Graph Convolution
论文作者
论文摘要
计划外的重症监护室（ICU）再入院率是评估医院护理质量的重要指标。有效，准确的ICU再入院风险预测不仅可以帮助防止患者出院和潜在危险，还可以降低医疗保健的相关成本。在本文中，我们提出了一种使用电子健康记录（EHR）医学文本进行预测的新方法，该方法为先前的研究提供了一种替代的观点，该研究在很大程度上依赖于患者的数值和时间序列特征。更具体地说，我们从EHR中提取患者的出院摘要，并用外部知识图增强的多视图图表。然后将图形卷积网络用于表示学习。实验结果证明了我们方法的有效性，为此任务产生了最先进的性能。
Unplanned intensive care unit (ICU) readmission rate is an important metric for evaluating the quality of hospital care. Efficient and accurate prediction of ICU readmission risk can not only help prevent patients from inappropriate discharge and potential dangers, but also reduce associated costs of healthcare. In this paper, we propose a new method that uses medical text of Electronic Health Records (EHRs) for prediction, which provides an alternative perspective to previous studies that heavily depend on numerical and time-series features of patients. More specifically, we extract discharge summaries of patients from their EHRs, and represent them with multiview graphs enhanced by an external knowledge graph. Graph convolutional networks are then used for representation learning. Experimental results prove the effectiveness of our method, yielding state-of-the-art performance for this task.
