预测急性心力衰竭入院:深度学习预测模型的开发,纳入气候信息
Takahiro Jimba1, Satoshi Kodera1, Shun Kohsaka2
1Tokyo CCU Network Scientific Committee, Tokyo, Japan; Department of Cardiovascular Medicine, Graduate School of Medicine and Faculty of Medicine, The University of Tokyo, Tokyo, Japan.
Journal of cardiac failure
|November 12, 2023
概括
气候影响心血管事件,但传统模型难以预测. 一个深度神经网络 (DNN) 模型有效地使用气候数据预测急性心力衰竭 (AHF) 病例,优于旧方法.
科学领域:
- 心脏病学 心脏病学
- 环境健康 环境健康
- 数据科学数据科学数据科学
背景情况:
- 气候变化被认为是影响心血管事件发生率的一个因素.
- 传统的统计模型在准确预测这些事件方面存在局限性.
研究的目的:
- 根据气候信息开发和评估一个深度神经网络 (DNN) 模型,用于预测急性心力衰竭 (AHF) 病例.
- 将DNN模型的预测性能与传统的回归模型进行比较.
主要方法:
- 来自东京CCU网络数据库 (2014-2019) 的27799例急性心力衰竭病例的分析.
- 使用气象参数和入学数据 (2014-2018) 开发DNN模型和传统回归模型.
- 在预测2019年AHF入院和高风险AHF日的模型性能评估中,使用Shapley对变量重要性的附加解释.
主要成果:
- DNN模型显示出与观察到的AHF入院有很强的相关性 (R2 = 0.413).
- 在预测高风险AHF日 (c-统计:0.888与0.827,P = .0013) 中,DNN模型显著优于后勤回归.
- 最低环境温度的7天移动平均值成为最重要的预测变量.
结论:
- 深度神经网络模型显示,使用气候数据预测发生的急性心力衰竭病例具有显著的前景.
- 根据气候信息预测AHF的入院情况,可以帮助有效管理急性心力衰竭.
- 这种方法提供了一种更精确的方法来预测与环境因素相关的患者流入.
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