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

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 b

SUBMITTER: Giannoula A 

PROVIDER: S-EPMC11759715 | biostudies-literature | 2025 Jan

REPOSITORIES: biostudies-literature