  A large fraction of the electronic health records (EHRs) consists of clinical
measurements collected over time, such as lab tests and vital signs, which
provide important information about a patient's health status. These sequences
of clinical measurements are naturally represented as time series,
characterized by multiple variables and large amounts of missing data, which
complicate the analysis. In this work, we propose a novel kernel which is
capable of exploiting both the information from the observed values as well the
information hidden in the missing patterns in multivariate time series (MTS)
originating e.g. from EHRs. The kernel, called TCK$_{IM}$, is designed using an
ensemble learning strategy in which the base models are novel mixed mode
Bayesian mixture models which can effectively exploit informative missingness
without having to resort to imputation methods. Moreover, the ensemble approach
ensures robustness to hyperparameters and therefore TCK$_{IM}$ is particularly
well suited if there is a lack of labels - a known challenge in medical
applications. Experiments on three real-world clinical datasets demonstrate the
effectiveness of the proposed kernel.
