An unsupervised decision-support framework for multivariate biomarker analysis in athlete monitoring

arXiv cs.LG / 4/17/2026

📰 NewsIdeas & Deep AnalysisModels & Research

共有:

Key Points

The paper presents an unsupervised, multivariate decision-support framework for athlete monitoring that learns latent physiological states without requiring injury ground-truth labels.
It uses a modular pipeline operating in joint biomarker space, combining preprocessing, clinical safety screening, unsupervised clustering, and centroid-based interpretation to make results actionable.
Using amateur soccer players’ data from a competitive microcycle, the method separates coherent profiles that distinguish mechanical damage from metabolic stress while retaining homeostatic states.
Synthetic data augmentation and structural stability analyses (including hierarchical clustering and GMM-based approaches) are used to test robustness and scalability in higher-dimensional settings.
The framework is designed to detect “silent” risk phenotypes that univariate or binary risk models may miss, supporting more individualized monitoring and decision-making.

Abstract

Purpose. Athlete monitoring is constrained by small cohorts, heterogeneous biomarker scales, limited feasibility of repeated sampling, and the lack of reliable injury ground truth. These limitations reduce the interpretability and utility of traditional univariate and binary risk models. This study addresses these challenges by proposing an unsupervised multivariate framework to identify latent physiological states in athletes using real data. Methods. We propose a modular computational framework that operates in the joint biomarker space, integrating preprocessing, clinical safety screening, unsupervised clustering, and centroid-based physiological interpretation. Profiles are learned exclusively from amateur soccer players during a competitive microcycle. Synthetic data augmentation evaluates robustness and scalability. Ward hierarchical clustering supports monitoring and etiological differentiation, while Gaussian Mixture Models (GMM) enable structural stability analysis in high-dimensional settings. Results. The framework identifies coherent profiles that distinguish mechanical damage from metabolic stress while preserving homeostatic states. Synthetic data augmentation demonstrates feasibility and detection of latent silent risk phenotypes typically missed by univariate monitoring. Structural analyses indicate robustness under augmentation and higher-dimensional settings. Conclusion. The framework enables interpretable identification of latent physiological states from multivariate biomarker data without injury labels. By distinguishing mechanisms and revealing silent risk patterns not captured by conventional monitoring, it provides actionable insights for individualized athlete monitoring and decision making.