MedSkim: Denoised Health Risk Prediction via Skimming Medical Claims Data-猫眼课题宝

DOI：

10.1109/icdm54844.2022.00018

发表时间：

2022-11

期刊：

2022 IEEE International Conference on Data Mining (ICDM)

影响因子：

通讯作者：

中科院分区：

其他

文献类型：

作者：

Suhan Cui;Junyu Luo;Muchao Ye;Jiaqi Wang;Ting Wang;Fenglong Ma

健康风险预测是一项具有挑战性的任务，旨在根据患者的历史EHR数据预测患者在不久的将来是否会患有某种疾病/病症。虽然现有的方法可以达到更好的性能，他们都不能处理的噪声存在于EHR数据显式。在本文中，我们假设自动去除噪声的EHR数据应该有助于模型进一步提高性能。相应地，我们提出了一种新的模型MedSkim，它能够自动排除不相关的访问和代码，通过有效地浏览EHR数据。特别是，所提出的模型具有一个代码选择模块，可以直接作出跳过决定，每个单独的诊断代码，然后删除目标无关的。反向探测RNN（BPRNN）旨在反向处理EHR数据并为访问提供粗粒度的表示学习。此外，前向跳跃RNN（FSRNN）提出了一种阅读EHR在前面的方式和动态选择重要的访问和代码的基础上，前两个模块的结果。最后，风险预测模块使用FSRNN的输出隐藏状态来生成最终表示以进行预测。此外，我们还根据模型的跳跃率设计了一个额外的正则化项，并将其与标准交叉熵损失联合收割机结合起来，以在端到端设置中训练模型。实验结果表明，MedSkim在三个真实世界的数据集上实现了最好的性能，与最先进的基线相比，在PR-AUC，F1和Cohen's Kappa。此外，消融研究和案例研究证实，所提出的MedSkim是合理和有效的，以消除噪音从EHR数据1.1所提出的MedSkim的源代码是在https://github.com/SH-Src/MedSkim

Health risk prediction is a challenge task that aims to predict whether patients would suffer from a certain disease/condition in the near future based on their historical EHR data. Although existing approaches can achieve better performance, none of them can deal with the noise existing in the EHR data explicitly. In this paper, we hypothesize that automatically removing noise from EHR data should help the models further improve the performance. Correspondingly, we propose a novel model named MedSkim, which is able to automatically rule out irrelevant visits and codes by effectively skimming through the EHR data. In particular, the proposed model has a code selection module that can directly make a skipping decision to each individual diagnosis codes and then remove the target-irrelevant ones. A backward probing RNN (BPRNN) is designed to reversely process the EHR data and provide a coarse grained representation learning for visits. Besides, a forward skipping RNN (FSRNN) is proposed to read the EHR in a preceding way and dynamically select important visits and codes based on the results of previous two modules. Finally, the risk prediction module uses the output hidden states from FSRNN for generating the final representation to make predictions. Additionally, we also design an extra regularization term based on the skip rate of the model and combine it with standard cross entropy loss to train the model in an end-to-end setting. Experimental results show that MedSkim achieves the best performance on three real-world datasets compared with the state-of-the-art baselines in terms of PR-AUC, F1 and Cohen’s Kappa. Moreover, the ablation study and case study confirm that the proposed MedSkim is reasonable and effective for removing noise from EHR data 1.1The source code of the proposed MedSkim is available at https://github.com/SH-Src/MedSkim

MedSkim: Denoised Health Risk Prediction via Skimming Medical Claims Data

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