融合病历文本与电子病历数据，提升30天再入院预测准确率
Prediction of 30-day hospital readmission with clinical notes and EHR information
- 构建图神经网络，将临床笔记与电子病历作为节点联合建模
- 模型在测试集上达到0.72 AUROC和66.7%平衡准确率
- 适合关注医疗预测、多模态数据融合的临床研究者使用
高再入院率带来显著医疗成本与患者健康风险，因此开发能预测患者30天内是否再入院的模型至关重要。如今可获取患者住院事件的结构化（电子健康记录 - EHR）与非结构化信息（临床笔记），两者均含潜在预测价值，但融合困难。本文探索将临床笔记与EHR结合用于预测30天再入院。通过图神经网络（GNN）表示EHR中各类信息，并利用大语言模型（LLM）刻画临床笔记内容。将两类信息作为图节点输入模型，最终实现0.72的AUROC与66.7%的平衡准确率，验证了多模态信息融合的有效性。
原文摘要 · Abstract (English)
High hospital readmission rates are associated with significant costs and health risks for patients. Therefore, it is critical to develop predictive models that can support clinicians to determine whether or not a patient will return to the hospital in a relatively short period of time (e.g, 30-days). Nowadays, it is possible to collect both structured (electronic health records - EHR) and unstructured information (clinical notes) about a patient hospital event, all potentially containing relevant information for a predictive model. However, their integration is challenging. In this work we explore the combination of clinical notes and EHRs to predict 30-day hospital readmissions. We address the representation of the various types of information available in the EHR data, as well as exploring LLMs to characterize the clinical notes. We collect both information sources as the nodes of a graph neural network (GNN). Our model achieves an AUROC of 0.72 and a balanced accuracy of 66.7\%, highlighting the importance of combining the multimodal information.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。