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arXiv 2408.07084cs.LGcs.AI
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动态超图增强的连续就医预测

Dynamic Hypergraph-Enhanced Prediction of Sequential Medical Visits

University of Southern California(南加州大学);University of California, Berkeley(加利福尼亚大学伯克利分校);Texas Tech University(德克萨斯理工大学);Southern Methodist University(南卫理公会大学)

Wangying Yang, Zitao Zheng, Zhizhong Wu, Bo Zhang, Yuanfang Yang

AI总结:

提出DHCE动态超图模型,通过区分急慢性病并融合医学语言模型编码的临床事件,提升电子健康记录中的连续诊断预测性能。

摘要:

本研究提出了一种开创性的动态超图网络(DHCE)模型,旨在从电子健康记录中预测未来的医疗诊断,并提升准确性。DHCE模型的创新之处在于识别并区分患者就诊历史中的急性和慢性疾病,构建动态超图以捕捉疾病之间复杂的高阶交互。它通过有效整合临床事件数据(体现为医学语言模型辅助编码)形成稳健的患者表示,从而超越传统循环神经网络和图神经网络。通过在MIMIC-III和MIMIC-IV两个基准数据集上的大量实验,DHCE模型表现出优越性能,在连续诊断预测的精确率方面显著优于已有基线模型。

英文摘要:

This study introduces a pioneering Dynamic Hypergraph Networks (DHCE) model designed to predict future medical diagnoses from electronic health records with enhanced accuracy. The DHCE model innovates by identifying and differentiating acute and chronic diseases within a patient's visit history, constructing dynamic hypergraphs that capture the complex, high-order interactions between diseases. It surpasses traditional recurrent neural networks and graph neural networks by effectively integrating clinical event data, reflected through medical language model-assisted encoding, into a robust patient representation. Through extensive experiments on two benchmark datasets, MIMIC-III and MIMIC-IV, the DHCE model exhibits superior performance, significantly outpacing established baseline models in the precision of sequential diagnosis prediction.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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