基于云平台的深度学习系统，可高效预测糖尿病风险并降低发病率。
Optimization and Application of Cloud-based Deep Learning Architecture for Multi-Source Data Prediction
- 利用AWS云平台和GPU加速训练，提升效率93.2%。
- 临床预测准确率达89.8%，敏感度92.3%，特异度95.1%。
- 适合大规模糖尿病预防，具显著公共卫生价值。
本研究构建了一个基于云平台的深度学习系统，用于早期预测糖尿病。系统依托AWS云平台的分布式计算能力，采用EC2 p3.8xlarge GPU实例加速模型训练，使训练时间缩短93.2%，同时保持94.2%的预测准确率。通过Apache Airflow构建自动化数据处理与模型训练流水线，实现端到端更新仅需18.7小时。在临床应用中，系统预测准确率为89.8%，敏感度为92.3%，特异度为95.1%。基于预测结果的早期干预使目标人群糖尿病发病率降低37.5%。系统高性能与可扩展性为大规模糖尿病防控提供有力支持，展现显著公共健康价值。
原文摘要 · Abstract (English)
This study develops a cloud-based deep learning system for early prediction of diabetes, leveraging the distributed computing capabilities of the AWS cloud platform and deep learning technologies to achieve efficient and accurate risk assessment. The system utilizes EC2 p3.8xlarge GPU instances to accelerate model training, reducing training time by 93.2% while maintaining a prediction accuracy of 94.2%. With an automated data processing and model training pipeline built using Apache Airflow, the system can complete end-to-end updates within 18.7 hours. In clinical applications, the system demonstrates a prediction accuracy of 89.8%, sensitivity of 92.3%, and specificity of 95.1%. Early interventions based on predictions lead to a 37.5% reduction in diabetes incidence among the target population. The system's high performance and scalability provide strong support for large-scale diabetes prevention and management, showcasing significant public health value.
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