ArticleBMC medical research methodology2026
Clustering longitudinal multivariate trajectories using an ensemble of principal component trees.
Article in BMC medical research methodology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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4 authors.
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Abstract
purposeClustering longitudinal data is challenging, particularly when measurements are high-dimensional, irregularly sampled, or noisy. We aim to provide a flexible and interpretable framework for identifying meaningful temporal subgroups and their key features.
methodsWe introduce TAPIO, an ensemble-based clustering approach with longitudinal extensions, including longTAPIO
resultsSimulation studies demonstrate that TAPIO accurately recovers cluster structure, identifies relevant features, and performs competitively with existing methods. longTAPIO
conclusionTAPIO offers a scalable, interpretable framework for longitudinal clustering that accommodates complex multivariate trajectories and high-dimensional data. Its ability to identify both meaningful clusters and their defining features has potential to advance patient stratification, biomarker discovery, and longitudinal data analysis in biomedical research.
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