Article: Intelligent monitoring of patient vital signs based on adaptive attention fusion spatiotemporal graph neural network Journal: International Journal of Ad Hoc and Ubiquitous Computing (IJAHUC) 2026 Vol.52 No.5 pp.48 - 62 Abstract: This study proposes a vital signs monitoring framework that addresses the limitations of traditional threshold-based alarms and existing deep-learning models in capturing multimodal physiological interactions and spatiotemporal dynamics. The method integrates an adaptive attention fusion mechanism that dynamically adjusts the importance of heterogeneous physiological parameters, a spatiotemporal graph neural network that jointly models inter-parameter correlations and temporal evolution using multi-scale windows, and a reinforcement learning module that enables active, strategy-driven early warning and clinical decision support. Evaluated on the MIMIC-III and eICU datasets, the proposed system achieves 96.3% anomaly detection accuracy, 38.5-minute early warning capability, and a 0.912 F1-score, outperforming existing methods. Ablation studies confirm the contributions of adaptive fusion, spatiotemporal graph modelling and policy optimisation. Inderscience Publishers - linking academia, business and industry through research

Title: Intelligent monitoring of patient vital signs based on adaptive attention fusion spatiotemporal graph neural network

Authors: Shunda Cheng; Jie Zhu; Shengjiang Guan; Jie Cheng; Tong Dou

Addresses: IT Department, Hebei Provincial Hospital of Chinese Medicine, Shijiazhuang, Heibei, 050011, China ' IT Department, Hebei Provincial Hospital of Chinese Medicine, Shijiazhuang, Heibei, 050011, China ' Hospital Leaders, Hebei Provincial Hospital of Chinese Medicine, Shijiazhuang, Heibei, 050011, China ' Pharmaceutical Department, Hebei Provincial Hospital of Chinese Medicine, Shijiazhuang, Heibei, 050011, China ' IT Department, Hebei Provincial Hospital of Chinese Medicine, Shijiazhuang, Heibei, 050011, China

Abstract: This study proposes a vital signs monitoring framework that addresses the limitations of traditional threshold-based alarms and existing deep-learning models in capturing multimodal physiological interactions and spatiotemporal dynamics. The method integrates an adaptive attention fusion mechanism that dynamically adjusts the importance of heterogeneous physiological parameters, a spatiotemporal graph neural network that jointly models inter-parameter correlations and temporal evolution using multi-scale windows, and a reinforcement learning module that enables active, strategy-driven early warning and clinical decision support. Evaluated on the MIMIC-III and eICU datasets, the proposed system achieves 96.3% anomaly detection accuracy, 38.5-minute early warning capability, and a 0.912 F1-score, outperforming existing methods. Ablation studies confirm the contributions of adaptive fusion, spatiotemporal graph modelling and policy optimisation.

Keywords: adaptive attention mechanism; spatiotemporal graph neural network; ST-GNN; vital sign monitoring; deep reinforcement learning; multimodal fusion.

DOI: 10.1504/IJAHUC.2026.154093

International Journal of Ad Hoc and Ubiquitous Computing, 2026 Vol.52 No.5, pp.48 - 62

Received: 16 Oct 2025
Accepted: 22 Dec 2025
Published online: 12 Jun 2026 *

Title: Intelligent monitoring of patient vital signs based on adaptive attention fusion spatiotemporal graph neural network

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