Application of the Signature Method to Pattern Recognition in the CEQUEL Clinical Trial
This work addresses pattern recognition in clinical trial data for bipolar disorder, but it appears incremental as it applies an existing method to a new domain.
The authors tackled the problem of classifying streaming data, specifically delays in responding to prompts from bipolar disorder patients in a clinical trial, by applying the signature method as a non-parametric approach, demonstrating its ability to extract features from sequential data.
The classification procedure of streaming data usually requires various ad hoc methods or particular heuristic models. We explore a novel non-parametric and systematic approach to analysis of heterogeneous sequential data. We demonstrate an application of this method to classification of the delays in responding to the prompts, from subjects with bipolar disorder collected during a clinical trial, using both synthetic and real examples. We show how this method can provide a natural and systematic way to extract characteristic features from sequential data.