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<p>The rapid growth of high-resolution medical time-series data presents significant challenges for both efficient processing and clinical interpretability. We propose an interpretable data-processing architecture that transforms large-scale physiological data into compact, clinically meaningful representations. Our approach is built on a <i>filter-centric framework</i> in which simple, computationally efficient conditions are systematically constructed to capture salient patterns in patient trajectories. <i>Filters</i> are evaluated through an exhaustive and reproducible search procedure, enabling the identification of highly discriminative patterns without relying on opaque black-box models. To ensure reliability in clinical applications, we introduce a principled selection strategy that prioritizes filters <i>achieving perfect precision while maximizing coverage</i>, thereby identifying patient groups with highly consistent outcomes and providing transparent, trustworthy decision support. To support large-scale deployment, we develop a multi-resolution indexing and scoring mechanism that accommodates incremental updates as new data are collected, allowing continuous integration of longitudinal patient records. We demonstrate the effectiveness of the framework using the suppression ratio as an illustrative feature; however, the methodology is general and applicable to a wide range of physiological signals and time-series representations. Our results confirm that the proposed architecture delivers scalable, interpretable, and clinically meaningful analysis of complex medical time-series data.</p>

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Strong filters: Toward an interpretable architecture for large-scale health data

  • 期刊论文

  • 发表日期：

    2026年6月25日

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  • Vladimir I. Zadorozhny,
  • Zhixiang Liu,
  • Jonathan Elmer

摘要

The rapid growth of high-resolution medical time-series data presents significant challenges for both efficient processing and clinical interpretability. We propose an interpretable data-processing architecture that transforms large-scale physiological data into compact, clinically meaningful representations. Our approach is built on a filter-centric framework in which simple, computationally efficient conditions are systematically constructed to capture salient patterns in patient trajectories. Filters are evaluated through an exhaustive and reproducible search procedure, enabling the identification of highly discriminative patterns without relying on opaque black-box models. To ensure reliability in clinical applications, we introduce a principled selection strategy that prioritizes filters achieving perfect precision while maximizing coverage, thereby identifying patient groups with highly consistent outcomes and providing transparent, trustworthy decision support. To support large-scale deployment, we develop a multi-resolution indexing and scoring mechanism that accommodates incremental updates as new data are collected, allowing continuous integration of longitudinal patient records. We demonstrate the effectiveness of the framework using the suppression ratio as an illustrative feature; however, the methodology is general and applicable to a wide range of physiological signals and time-series representations. Our results confirm that the proposed architecture delivers scalable, interpretable, and clinically meaningful analysis of complex medical time-series data.