Variable Selection Methods for Multivariate, Functional, and Complex Biomedical Data in the AI Age
This work addresses variable selection for professionals in biostatistics, statistics, and AI dealing with multivariate, functional, and complex data in personalized medicine and digital health, representing an incremental advancement with a novel method for a known bottleneck.
The authors tackled the problem of variable selection for complex biomedical data like functional biomarkers by proposing a new optimization-based framework that applies to various regression models and data types. Their method outperforms state-of-the-art approaches in accuracy and speed, achieving several orders of magnitude improvement in speed across different statistical responses.
Many problems within personalized medicine and digital health rely on the analysis of continuous-time functional biomarkers and other complex data structures emerging from high-resolution patient monitoring. In this context, this work proposes new optimization-based variable selection methods for multivariate, functional, and even more general outcomes in metrics spaces based on best-subset selection. Our framework applies to several types of regression models, including linear, quantile, or non parametric additive models, and to a broad range of random responses, such as univariate, multivariate Euclidean data, functional, and even random graphs. Our analysis demonstrates that our proposed methodology outperforms state-of-the-art methods in accuracy and, especially, in speed-achieving several orders of magnitude improvement over competitors across various type of statistical responses as the case of mathematical functions. While our framework is general and is not designed for a specific regression and scientific problem, the article is self-contained and focuses on biomedical applications. In the clinical areas, serves as a valuable resource for professionals in biostatistics, statistics, and artificial intelligence interested in variable selection problem in this new technological AI-era.