Automatic Classification of Irregularly Sampled Time Series with Unequal Lengths: A Case Study on Estimated Glomerular Filtration Rate
This work addresses the challenge of quickly evaluating kidney disease risk in large patient populations by automating a traditionally manual process, though it is incremental as it adapts existing methods to a specific domain.
The authors tackled the problem of automating classification of irregularly sampled eGFR time series with unequal lengths by developing a two-tier system using Gaussian process regression and K-NN/SVM, achieving an F-score of 0.90 compared to 0.96 for human experts.
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.