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Clinical measurements collected over time are naturally represented asmultivariate time series (MTS), which often contain missing data. Anautoencoder can learn low dimensional vectorial representations of MTS thatpreserve important data characteristics, but cannot deal explicitly withmissing data. In this work, we propose a new framework that combines anautoencoder with the Time series Cluster Kernel (TCK), a kernel that accountsfor missingness patterns in MTS. Via kernel alignment, we incorporate TCK inthe autoencoder to improve the learned representations in presence of missingdata. We consider a classification problem of MTS with missing values,representing blood samples of patients with surgical site infection. With ourapproach, rather than with a standard autoencoder, we learn representations inlow dimensions that can be classified better.

Learning compressed representations of blood samples time series with missing data