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A large fraction of the electronic health records consists of clinicalmeasurements collected over time, such as blood tests, which provide importantinformation about the health status of a patient. These sequences of clinicalmeasurements are naturally represented as time series, characterized bymultiple variables and the presence of missing data, which complicate analysis.In this work, we propose a surgical site infection detection framework forpatients undergoing colorectal cancer surgery that is completely unsupervised,hence alleviating the problem of getting access to labelled training data. Theframework is based on powerful kernels for multivariate time series thataccount for missing data when computing similarities. Our approach showsuperior performance compared to baselines that have to resort to imputationtechniques and performs comparable to a supervised classification baseline.

An Unsupervised Multivariate Time Series Kernel Approach for Identifying Patients with Surgical Site Infection from Blood Samples