A Prior-knowledge-guided Dynamic Attention Mechanism to Predict Nocturnal Hypoglycemic Events in Type 1 Diabetes

Overview

Journal BMC Med Inform Decis Mak

Publisher Biomed Central

Specialty Medical Informatics

Date 2024 Dec 19

PMID 39696373

Authors

Xia Yu

Zi Yang

Xinzhuo Wang

Xiaoyu Sun

Ruiting Shen

Hongru Li

Mingchen Zhang

Affiliations

Soon will be listed here.

Abstract

Nocturnal hypoglycemia is a critical problem faced by diabetic patients. Failure to intervene in time can be dangerous for patients. The existing early warning methods struggle to extract crucial information comprehensively from complex multi-source heterogeneous data. In this paper, a deep learning framework with an innovative dynamic attention mechanism is proposed to predict nocturnal hypoglycemic events for type 1 diabetes patients. Features related to nocturnal hypoglycemia are extracted from multi-scale and multi-dimensional data, which enables comprehensive information extraction from diverse sources. Then, we propose a prior-knowledge-guided attention mechanism to enhance the network's learning capability and interpretability. The method was evaluated on a public available clinical dataset, which successfully warned 94.91% of nocturnal hypoglycemic events with an F1-score of 96.35%. By integrating our proposed framework into the nocturnal hypoglycemia early warning model, issues related to feature redundancy and incompleteness were mitigated. Comparative analysis demonstrates that our method outperforms existing approaches, offering superior accuracy and practicality in real-world scenarios.

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