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

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dc.contributor.author Giannoula, Alexia
dc.contributor.author Gutiérrez Sacristán, Alba
dc.contributor.author Bravo Serrano, Àlex, 1984-
dc.contributor.author Sanz, Ferran
dc.contributor.author Furlong, Laura I., 1971-
dc.date.accessioned 2018-05-10T08:19:43Z
dc.date.available 2018-05-10T08:19:43Z
dc.date.issued 2018
dc.identifier.citation Giannoula A, Gutierrez-Sacristán A, Bravo Á, Sanz F, Furlong LI. Identifying temporal patterns in patient disease trajectories using dynamic time warping: A population-based study. Sci Rep 2018 Dec; 8(1): 4216. DOI: 10.1038/s41598-018-22578-1
dc.identifier.issn 2045-2322
dc.identifier.uri http://hdl.handle.net/10230/34608
dc.description.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.
dc.description.sponsorship We received support from ISCIII-FEDER (PI13/00082, CP10/00524, CPII16/00026), IMI-JU under grants agreements no. 115372 (EMIF), resources composed of financial contribution from the EU-FP7 (FP7/2007-2013) and EFPIA companies in kind contribution, and the EU H2020 Programme 2014-2020 under grant agreements no. 634143 (MedBioinformatics) and no. 676559 (Elixir-Excelerate). The Research Programme on Biomedical Informatics (GRIB) is a member of the Spanish National Bioinformatics Institute (INB), PRB2-ISCIII and is supported by grant PT13/0001/0023, of the PE I + D + i 2013-2016, funded by ISCIII and FEDER. Funding has been also received by the Marie-Curie UPFellowship Program.
dc.format.mimetype application/pdf
dc.language.iso eng
dc.publisher Nature Publishing Group
dc.relation.ispartof Scientific Reports. 2018 Dec;8(1):4216
dc.rights © The Author(s) 2018. Open Access. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, 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 license, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons license 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 license, visit http://creativecommons.org/licenses/by/4.0/.
dc.rights.uri http://creativecommons.org/licenses/by/4.0/
dc.title Identifying temporal patterns in patient disease trajectories using dynamic time warping: A population-based study
dc.type info:eu-repo/semantics/article
dc.identifier.doi http://dx.doi.org/10.1038/s41598-018-22578-1
dc.subject.keyword Dynamic time warping
dc.subject.keyword Translational research
dc.subject.keyword Machine learning
dc.relation.projectID info:eu-repo/grantAgreement/EC/FP7/115372
dc.relation.projectID info:eu-repo/grantAgreement/EC/H2020/634143
dc.relation.projectID info:eu-repo/grantAgreement/EC/H2020/676559
dc.rights.accessRights info:eu-repo/semantics/openAccess
dc.type.version info:eu-repo/semantics/publishedVersion

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