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A deep learning–based, unsupervised method to impute missing values in electronic health records for improved patient management
Journal of Biomedical informatics ( IF 4.5 ) Pub Date : 2020-10-01 , DOI: 10.1016/j.jbi.2020.103576
Da Xu , Paul Jen-Hwa Hu , Ting-Shuo Huang , Xiao Fang , Chih-Chin Hsu

Electronic health records (EHRs) often suffer missing values, for which recent advances in deep learning offer a promising remedy. We develop a deep learning–based, unsupervised method to impute missing values in patient records, then examine its imputation effectiveness and predictive efficacy for peritonitis patient management. Our method builds on a deep autoencoder framework, incorporates missing patterns, accounts for essential relationships in patient data, considers temporal patterns common to patient records, and employs a novel loss function for error calculation and regularization. Using a data set of 27,327 patient records, we perform a comparative evaluation of the proposed method and several prevalent benchmark techniques. The results indicate the greater imputation performance of our method relative to all the benchmark techniques, recording 5.3–15.5% lower imputation errors. Furthermore, the data imputed by the proposed method better predict readmission, length of stay, and mortality than those obtained from any benchmark techniques, achieving 2.7–11.5% improvements in predictive efficacy. The illustrated evaluation indicates the proposed method’s viability, imputation effectiveness, and clinical decision support utilities. Overall, our method can reduce imputation biases and be applied to various missing value scenarios clinically, thereby empowering physicians and researchers to better analyze and utilize EHRs for improved patient management.



中文翻译:

一种基于深度学习的无监督方法,可估算电子健康记录中的缺失值,以改善患者管理

电子健康记录(EHR)经常会缺少价值,为此,深度学习的最新进展提供了一种有希望的补救方法。我们开发了一种基于深度学习的无监督方法来估算患者记录中的缺失值,然后检查其对腹膜炎患者管理的估算效果和预测效果。我们的方法建立在深层的自动编码器框架上,结合了缺失的模式,说明了患者数据中的基本关系,考虑了患者记录所共有的时间模式,并采用了一种新颖的损失函数进行错误计算和正则化。使用27,327个患者记录的数据集,我们对提出的方法和几种流行的基准技术进行了比较评估。结果表明,相对于所有基准测试技术,我们的方法具有更高的插补性能,记录的插补误差降低了5.3–15.5%。此外,与从任何基准技术获得的数据相比,通过拟议方法估算的数据可更好地预测再入院率,住院时间和死亡率,从而使预测功效提高了2.7-11.5%。图示的评估结果表明了所提出方法的可行性,估算效率和临床决策支持效用。总体而言,我们的方法可以减少归因偏差,并在临床上应用于各种缺失价值的情况,从而使医生和研究人员能够更好地分析和利用EHR来改善患者管理。预测功效提高5%。图示的评估结果表明了所提出方法的可行性,估算效率和临床决策支持效用。总体而言,我们的方法可以减少归因偏差,并在临床上应用于各种缺失价值的情况,从而使医生和研究人员能够更好地分析和利用EHR来改善患者管理。预测功效提高5%。图示的评估结果表明了所提出方法的可行性,估算效率和临床决策支持效用。总体而言,我们的方法可以减少归因偏差,并在临床上应用于各种缺失价值的情况,从而使医生和研究人员能够更好地分析和利用EHR来改善患者管理。

更新日期:2020-10-17
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