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