Identifying temporal patterns in patient disease trajectories using dynamic time warping: A population-based study

75Citations
Citations of this article
176Readers
Mendeley users who have this article in their library.

This article is free to access.

Abstract

Time is a crucial parameter in the assessment of comorbidities in population-based studies, as it permits to identify more complex disease patterns apart from the pairwise disease associations. So far, it has been, either, completely ignored or only, taken into account by assessing the temporal directionality of identified comorbidity pairs. In this work, a novel time-analysis framework is presented for large-scale comorbidity studies. The disease-history vectors of patients of a regional Spanish health dataset are represented as time sequences of ordered disease diagnoses. Statistically significant pairwise disease associations are identified and their temporal directionality is assessed. Subsequently, an unsupervised clustering algorithm, based on Dynamic Time Warping, is applied on the common disease trajectories in order to group them according to the temporal patterns that they share. The proposed methodology for the temporal assessment of such trajectories could serve as the preliminary basis of a disease prediction system.

Cite

CITATION STYLE

APA

Giannoula, A., Gutierrez-Sacristán, A., Bravo, Á., Sanz, F., & Furlong, L. I. (2018). Identifying temporal patterns in patient disease trajectories using dynamic time warping: A population-based study. Scientific Reports, 8(1). https://doi.org/10.1038/s41598-018-22578-1

Register to see more suggestions

Mendeley helps you to discover research relevant for your work.

Already have an account?

Save time finding and organizing research with Mendeley

Sign up for free