  A patient's estimated glomerular filtration rate (eGFR) can provide important
information about disease progression and kidney function. Traditionally, an
eGFR time series is interpreted by a human expert labelling it as stable or
unstable. While this approach works for individual patients, the time consuming
nature of it precludes the quick evaluation of risk in large numbers of
patients. However, automating this process poses significant challenges as eGFR
measurements are usually recorded at irregular intervals and the series of
measurements differs in length between patients. Here we present a two-tier
system to automatically classify an eGFR trend. First, we model the time series
using Gaussian process regression (GPR) to fill in `gaps' by resampling a fixed
size vector of fifty time-dependent observations. Second, we classify the
resampled eGFR time series using a K-NN/SVM classifier, and evaluate its
performance via 5-fold cross validation. Using this approach we achieved an
F-score of 0.90, compared to 0.96 for 5 human experts when scored amongst
themselves.
