Identifying time patterns in Huntington's disease trajectories using dynamic time warping-based clustering on multi-modal data

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• dc.contributor.author Giannoula, Alexia
• dc.contributor.author De Paepe, Audrey E.
• dc.contributor.author Sanz, Ferran
• dc.contributor.author Furlong, Laura I., 1971-
• dc.contributor.author Càmara, Estela
• dc.date.accessioned 2025-03-24T07:14:43Z
• dc.date.available 2025-03-24T07:14:43Z
• dc.date.issued 2025
• dc.description.abstract One of the principal goals of Precision Medicine is to stratify patients by accounting for individual variability. However, extracting meaningful information from Real-World Data, such as Electronic Health Records, still remains challenging due to methodological and computational issues. A Dynamic Time Warping-based unsupervised-clustering methodology is presented in this paper for the clustering of patient trajectories of multi-modal health data on the basis of shared temporal characteristics. Building on an earlier methodology, a new dimension of time-varying clinical and imaging features is incorporated, through an adapted cost-minimization algorithm for clustering on different, possibly overlapping, feature subsets. The model disease chosen is Huntington's disease (HD), characterized by progressive neurodegeneration. From a wide range of examined user-defined parameters, four case examples are highlighted to demonstrate the identified temporal patterns in multi-modal HD trajectories and to study how these differ due to the combined effects of feature weights and granularity threshold. For each identified cluster, polynomial fits that describe the time behavior of the assessed features are provided for an informative comparison, together with their averaged values. The proposed data-mining methodology permits the stratification of distinct time patterns of multi-modal health data in individuals that share a diagnosis, by employing user-customized criteria beyond the current clinical practice. Overall, this work bears implications for better analysis of individual variability in disease progression, opening doors to personalized preventative, diagnostic and therapeutic strategies.
• dc.description.sponsorship IMPaCT-Data (IMP/00019) funded by the Institute of Health Carlos III, co-funded by the European Union, European Regional Development Fund (ERDF, “A way to make Europe”). A. E. D. received funding from the Masters in Multidisciplinary Research in Experimental Sciences of the Barcelona Institute of Science and Technology and University of Pompeu Fabra. E. C. was supported by the Instituto de Salud Carlos III, an agency of the Ministerio de Ciencia, Innovacion y Universidades (MINECO), co-funded by FEDER funds/European Regional Development Fund (ERDF) – a Way to Build Europe (CP13/00225 and PI14/ 00834, to EC), as well as Ministerio de Ciencia e Innovación, which is part of Agencia Estatal de Investigación (AEI), through the Retos Investigación grant, number PID2020-114518RB-I00 / DOI: https://doi.org/10.13039/501100011033 to EC, BFU2017-87109-P, to Ruth de Diego. We thank CERCA Programme/Generalitat de Catalunya for institutional support.
• dc.format.mimetype application/pdf
• dc.identifier.citation Giannoula A, De Paepe AE, Sanz F, Furlong LI, Camara E. Identifying time patterns in Huntington's disease trajectories using dynamic time warping-based clustering on multi-modal data. Sci Rep. 2025 Jan 24;15(1):3081. DOI: 10.1038/s41598-025-86686-5
• dc.identifier.doi http://dx.doi.org/10.1038/s41598-025-86686-5
• dc.identifier.issn 2045-2322
• dc.identifier.uri http://hdl.handle.net/10230/69993
• dc.language.iso eng
• dc.publisher Nature Research
• dc.relation.ispartof Sci Rep. 2025 Jan 24;15(1):3081
• dc.relation.projectID info:eu-repo/grantAgreement/ES/2PE/PID2020-114518RB-I00
• dc.relation.projectID info:eu-repo/grantAgreement/ES/2PE/BFU2017-87109-P
• dc.rights © The Author(s) 2025. Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.
• dc.rights.accessRights info:eu-repo/semantics/openAccess
• dc.rights.uri http://creativecommons.org/licenses/by-nc-nd/4.0/
• dc.subject.keyword Longitudinal cohort analysis
• dc.subject.keyword Multi-modal Real-World Data
• dc.subject.keyword Patient stratification
• dc.subject.keyword Precision medicine
• dc.subject.keyword Time analysis
• dc.subject.keyword Unsupervised clustering
• dc.title Identifying time patterns in Huntington's disease trajectories using dynamic time warping-based clustering on multi-modal data
• dc.type info:eu-repo/semantics/article
• dc.type.version info:eu-repo/semantics/publishedVersion