Application of the Signature Method to Pattern Recognition in the CEQUEL Clinical Trial

TL;DR

This paper introduces a non-parametric signature method for classifying heterogeneous sequential data, demonstrated on clinical trial data for bipolar disorder response delays, offering a systematic feature extraction approach.

Contribution

The paper presents a novel application of the signature method to classify delays in clinical trial data, providing a systematic alternative to heuristic models.

Findings

Effective classification of clinical response delays

Systematic feature extraction from sequential data

Applicable to synthetic and real data

Abstract

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.