基于电子病历的临床预测超图变换器。

Ran Xu, Mohammed K Ali, Joyce C Ho, Carl Yang
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引用次数: 0

摘要

电子健康记录(EHR)数据包含有关患者健康状况的丰富信息,包括诊断、手术、用药等,这些信息已被广泛用于促进数字医疗。尽管电子病历非常重要,但由于每次就诊都包含大量不同的医疗代码,要学习有用的就诊表征以支持下游临床预测往往并非易事。因此,医疗代码之间复杂的相互作用往往无法捕捉,从而导致预测结果不达标。为了更好地模拟这些复杂的关系,我们利用超图来超越配对关系,共同学习就诊和医疗代码的表征。我们还建议使用自我关注机制,根据下游临床预测自动识别每次就诊最相关的医疗代码,从而获得更好的泛化能力。在两个电子病历数据集上的实验表明,我们提出的方法不仅性能优越,而且对目标任务提供了合理的见解。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Hypergraph Transformers for EHR-based Clinical Predictions.

Electronic health records (EHR) data contain rich information about patients' health conditions including diagnosis, procedures, medications and etc., which have been widely used to facilitate digital medicine. Despite its importance, it is often non-trivial to learn useful representations for patients' visits that support downstream clinical predictions, as each visit contains massive and diverse medical codes. As a result, the complex interactions among medical codes are often not captured, which leads to substandard predictions. To better model these complex relations, we leverage hypergraphs, which go beyond pairwise relations to jointly learn the representations for visits and medical codes. We also propose to use the self-attention mechanism to automatically identify the most relevant medical codes for each visit based on the downstream clinical predictions with better generalization power. Experiments on two EHR datasets show that our proposed method not only yields superior performance, but also provides reasonable insights towards the target tasks.

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