Open Access

ARTICLE

Sparse Physio-Attention: A Computationally Efficient and Clinically Interpretable Framework for ICU Time-Series Analysis

Hashim Ali^*

School of Computing and Artificial Intelligence, Nazarbayev University, Astana, Kazakhstan

* Corresponding Author: Hashim Ali. Email:

Computers, Materials & Continua 2026, 89(1), 94 https://doi.org/10.32604/cmc.2026.087214

Received 12 June 2026; Accepted 20 July 2026; Issue published 13 August 2026

Abstract

Intensive care unit (ICU) time series are irregular, incomplete, and computationally demanding to model at high temporal resolution. Dense Transformer attention captures long-range dependencies but evaluates all pairwise interactions, including many stable or clinically weak measurements. This study presents Sparse Physio-Attention, a physiology-guided Transformer that retains critical-range violations, patient-relative deviations, informative missingness patterns, and task-relevant variables before sparse attention is computed. Dynamic routing subsequently removes weak attention edges, and a late-fusion adapter incorporates static electronic health record context. The analysis included 25,368 eligible MIMIC-IV ICU stays, of which 2740 were sepsis positive. On the held-out test set, the proposed model achieved an AUROC of 0.892 and an AUPRC of 0.835 at the 24-h prediction horizon, with an expected calibration error of 0.031. Relative to dense attention, it reduced floating-point operations by approximately 72%, peak GPU memory from 3.2 to 1.8 GB, and 24-h-window latency from 670±24 to 235±11 ms. Attention-guideline agreement reached 0.826 at 24 h, indicating alignment with prespecified deterioration rules; this result reflects clinical plausibility rather than causal explanation. The evidence is limited to internal MIMIC-IV validation, and the rule-based retention thresholds may require recalibration across hospitals and patient subgroups. External and prospective validation is therefore required before clinical deployment.

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Keywords

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