使用个人环境传感器早期检测COPD患者的症状:使用线性动态系统的概率潜成分分析进行远程传感框架
Şefki Kolozali1, Lia Chatzidiakou2, Roderic Jones2
1School of Computer Science and Electronic Engineering, University of Essex, Colchester, UK.
Neural computing & applications
|July 17, 2023
概括
这项研究使用个人空气污染暴露数据提前一天预测慢性阻塞性肺病 (COPD) 症状. 环境空气质量指标在预测症状恶化方面与直接测量一样有效.
科学领域:
- 环境健康 环境健康
- 肺部医学 肺部医学
- 数据科学数据科学数据科学
背景情况:
- 慢性阻塞性肺病 (COPD) 的管理受到环境因素的挑战,特别是空气污染.
- 准确预测COPD恶化对于及时干预和改善患者结果至关重要.
- 现有的COPD远程监测系统通常使用基于树的分类器.
研究的目的:
- 开发和验证基于个人空气污染暴露的COPD症状预测系统.
- 评估高级建模技术的有效性,包括概率潜伏组件分析 (PLCA) 和线性动态系统 (LDS),用于COPD症状预测.
- 将新型模型与传统的随机森林 (RF) 模型的性能进行比较,以预测COPD症状恶化.
主要方法:
- 一组106名COPD患者使用便携式环境传感器节点监测空气污染和活动水平.
- 收集了每天的症状记录和峰值流量测量.
- 概率潜伏组件分析 (PLCA) 与线性动态系统 (LDS) 结合,用于个性化和人口监测,并与随机森林模型进行比较.
主要成果:
- 拟议的PLCA-LDS-3D模型表现出卓越的性能,平均比PLCA和RF模型表现出4-20%.
- 仅使用空气污染物作为输入,PLCA-LDS-3D模型在预测恶化的肺容量时达到48.67%的准确性,并在个性化模型中预测COPD症状恶化时达到38.67%的准确性.
- 环境空气质量指标被发现与直接测量一样可以预测呼吸道症状恶化.
结论:
- 开发的系统可以提前一天预测COPD症状,为患者管理提供了显著的进步.
- 该PLCA-LDS-3D模型代表了个性化和人口层面监测COPD患者的有希望的方法.
- 环境空气污染数据是COPD患者呼吸系统症状恶化的有价值和有效预测指标.
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