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

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 dire...

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Bibliographic Details
Published in:Scientific reports 2018-03, Vol.8 (1), p.4216-14, Article 4216
Main Authors: Giannoula, Alexia, Gutierrez-Sacristán, Alba, Bravo, Álex, Sanz, Ferran, Furlong, Laura I.
Format: Article
Language:English
Subjects:
631/114/1305
631/114/2164
631/114/2415
631/1647/48
692/308/575
Cluster Analysis
Comorbidity
Dynamic time warping
Epidemiology
Female
Humanities and Social Sciences
Humans
Machine learning
Male
Medical Informatics - methods
multidisciplinary
Population studies
Population-based studies
Registries
Science
Science (multidisciplinary)
Statistical analysis
Time Factors
Translational research
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Summary: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.
ISSN:2045-2322
2045-2322
DOI:10.1038/s41598-018-22578-1