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

2016/06/07 by Andrey Kormilitzin, Kate Saunders, Kormilitzin, A. B. +7 · 3 citations
Mathematics · Medicine · Psychology · #Applications (stat.AP) #Bipolar Disorder and Treatment #FOS: Computer and information sciences #Machine Learning (stat.ML) #Mental Health Research Topics #Statistical Methods in Clinical Trials

paper · pdf · doi:10.48550/arxiv.1606.02074

openalex publication_date 2016/06/07 · openalex created_date 2016/06/24 · openalex updated_date 2026/07/28

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.

Citations

• Learning from the past, predicting the statistics for the future, learning an evolving system
• The 16-Item quick inventory of depressive symptomatology (QIDS), clinician rating (QIDS-C), and self-report (QIDS-SR): a psychometric evaluation in patients with chronic major depression
• Character-level Chinese Writer Identification using Path Signature Feature, DropStroke and Deep CNN
• A Primer on the Signature Method in Machine Learning

Cited by

• Hedging with memory: shallow and deep learning with signatures
• Path Imputation Strategies for Signature Models of Irregular Time Series
• Detecting early signs of depressive and manic episodes in patients with bipolar disorder using the signature-based model

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