Clinical deep model to analyse medical multivariate time-series data for health diagnosis

Layth Almahadeen, Richa Vijay, Mohammad Shabaz, Mukesh Soni, Pavitar Parkash Singh, Pavan Patel and Haewon Byeon

Cyber-Physical Systems, 2025, vol. 11, issue 2, 139-164

Abstract: Clinical auxiliary decision-making is related to life and health of patients, so the deep model needs to extract the personalised representation of patients to ensure high analysis and prediction accuracy; and provide a basis for prediction conclusions. In this context, a clinical deep model proposed an interpretable assessment method of patient health status based on contextual learning of medical features, encoding the time-series features of each variable separately, and using a multi-head de-coordination self-attention mechanism for learning Relationships between different features; feature skip-connection encoding based on a compressed excitation mechanism is proposed to improve the sensitivity of the model.

Date: 2025
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DOI: 10.1080/23335777.2024.2329677

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