Submit

Login Login

Register Register

Home / Journals / CMC / Online First / doi:10.32604/cmc.2026.087214
Journal Menu
Special Issues
Table of Content

All issues

Online First

2026

2025

2024

2023

2022

2021

2020

2019

2018

2017

2016

2015

2014

2013

2012

2011

2010

2009

2008

2007

2006

2005

2004

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: email

Computers, Materials & Continua https://doi.org/10.32604/cmc.2026.087214

Received 12 June 2026; Accepted 20 July 2026; Published online 31 July 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.

Keywords

Intensive care unit; clinical time series; sparse attention; transformer; explainable artificial intelligence; clinical decision support; sepsis prediction

  • 135

    View

  • 20

    Download

  • 0

    Like

Related articles
Copyright© 2026 Tech Science Press
© 1997-2026 TSP (Henderson, USA) unless otherwise stated

Share Link