Electronic records contain sequences of events, some of which take place all
at once in a single visit, and others that are dispersed over multiple visits,
each with a different timestamp. We postulate that fine temporal detail, e.g.,
whether a series of blood tests are completed at once or in rapid succession
should not alter predictions based on this data. Motivated by this intuition,
we propose models for analyzing sequences of multivariate clinical time series
data that are invariant to this temporal clustering. We propose an efficient
data augmentation technique that exploits the postulated temporal-clustering
invariance to regularize deep neural networks optimized for several clinical
prediction tasks. We introduce two techniques to temporally coarsen
(downsample) irregular time series: (i) grouping the data points based on
regularly-spaced timestamps; and (ii) clustering them, yielding
irregularly-paced timestamps. Moreover, we propose a MultiResolution Ensemble
(MRE) model, improving predictive accuracy by ensembling predictions based on
inputs sequences transformed by different coarsening operators. Our experiments
show that MRE improves the mAP on the benchmark mortality prediction task from
51.53% to 53.92%.

本研究基于数据时间聚类的不变性，提出了分析多变量临床时间序列数据的模型以及一种数据扩充技术方案，用于规范化深度神经网络中医学预测任务，该方法以评估医疗预测任务为基准，提高了其预测精度。