用高效网络与数字孪生技术提升心脏病突发早期预警能力
EfficientNet in Digital Twin-based Cardiac Arrest Prediction and Analysis
- 结合EfficientNet与数字孪生，从物联网数据中学习心血管特征
- 系统在预测心脏骤停时准确率高且运行效率优
- 适合临床智能监护与个性化医疗研究者参考
心脏骤停是全球重大健康问题，早期识别与干预对改善预后至关重要。本文提出一种融合基于EfficientNet的深度学习模型与数字孪生系统的新型框架，用于提升心脏骤停的早期检测与分析能力。通过复合缩放策略和EfficientNet提取心血管影像特征，同时利用数字孪生技术基于患者可穿戴物联网设备采集的数据，构建个性化的实时心血管系统模型，实现对患者状态的持续评估及治疗方案影响的模拟。实验表明，该系统在预测准确性与计算效率方面均表现优异。结合深度学习与数字孪生技术，为心脏病的主动化、个性化预测提供了新路径。
原文摘要 · Abstract (English)
Cardiac arrest is one of the biggest global health problems, and early identification and management are key to enhancing the patient's prognosis. In this paper, we propose a novel framework that combines an EfficientNet-based deep learning model with a digital twin system to improve the early detection and analysis of cardiac arrest. We use compound scaling and EfficientNet to learn the features of cardiovascular images. In parallel, the digital twin creates a realistic and individualized cardiovascular system model of the patient based on data received from the Internet of Things (IoT) devices attached to the patient, which can help in the constant assessment of the patient and the impact of possible treatment plans. As shown by our experiments, the proposed system is highly accurate in its prediction abilities and, at the same time, efficient. Combining highly advanced techniques such as deep learning and digital twin (DT) technology presents the possibility of using an active and individual approach to predicting cardiac disease.
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