Title: Learning compressed representations of blood samples time series with missing data,
Abstract: Clinical measurements collected over time are naturally represented as
multivariate time series (MTS), which often contain missing data. An
autoencoder can learn low dimensional vectorial representations of MTS that
preserve important data characteristics, but cannot deal explicitly with
missing data. In this work, we propose a new framework that combines an
autoencoder with the Time series Cluster Kernel (TCK), a kernel that accounts
for missingness patterns in MTS. Via kernel alignment, we incorporate TCK in
the autoencoder to improve the learned representations in presence of missing
data. We consider a classification problem of MTS with missing values,
representing blood samples of patients with surgical site infection. With our
approach, rather than with a standard autoencoder, we learn representations in
low dimensions that can be classified better.