Medical time series of laboratory tests has been collected in electronic health records (EHRs) in many countries. Machine-learning algorithms have been proposed to analyze the condition of patients using these medical records. However, medical time series may be recorded using different laboratory parameters in different datasets. This results in the failure of applying a pretrained model on a test dataset containing a time series of different laboratory parameters. This article proposes to solve this problem with an unsupervised time-series adaptation method that generates time series across laboratory parameters. Specifically, a medical time-series generation network with similarity distillation is developed to reduce the domain gap caused by the difference in laboratory parameters. The relations of different laboratory parameters are analyzed, and the similarity information is distilled to guide the generation of target-domain specific laboratory parameters. To further improve the performance in cross-domain medical applications, a missingness-aware feature extraction network is proposed, where the missingness patterns reflect the health conditions and, thus, serve as auxiliary features for medical analysis. In addition, we also introduce domain-adversarial networks in both feature level and time-series level to enhance the adaptation across domains. Experimental results show that the proposed method achieves good performance on both private and publicly available medical datasets. Ablation studies and distribution visualization are provided to further analyze the properties of the proposed method.

中文翻译：

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医学应用中具有相似性蒸馏的跨域缺失感知时间序列适应

许多国家已在电子健康记录 (EHR) 中收集了实验室测试的医学时间序列。已经提出了机器学习算法来使用这些病历分析患者的状况。但是，可以使用不同数据集中的不同实验室参数记录医疗时间序列。这导致无法在包含不同实验室参数的时间序列的测试数据集上应用预训练模型。本文提出用一种无监督的时间序列适应方法来解决这个问题，该方法生成跨实验室参数的时间序列。具体来说，开发了一种具有相似性蒸馏的医学时间序列生成网络，以减少由实验室参数差异引起的域间隙。分析不同实验室参数的关系，并提取相似信息以指导目标域特定实验室参数的生成。为了进一步提高跨领域医疗应用的性能，提出了一种缺失感知特征提取网络，其中缺失模式反映了健康状况，因此可以作为医学分析的辅助特征。此外，我们还在特征级别和时间序列级别引入了域对抗网络，以增强跨域的适应能力。实验结果表明，所提出的方法在私有和公开可用的医疗数据集上均取得了良好的性能。提供消融研究和分布可视化以进一步分析所提出方法的特性。