在使用机器学习的网络物理系统中用于COPD管理的新框架
Navneet Kumar Rajpoot1, Prabh Deep Singh2, Bhaskar Pant3
1Department of Computer Science and Engineering, Graphic Era (Deemed to be University), Dehradun, Uttarakhand, India. navneetrajpootgeu@gmail.com.
Scientific reports
|October 21, 2025
概括
这项研究引入了一种新的网络物理系统框架,用于实时预测慢性阻塞性肺病 (COPD) 恶化情况,使用多种数据源和机器学习进行早期检测和改善患者护理.
科学领域:
- 计算机科学与工程 计算机科学与工程
- 医疗信息学 医疗信息学
- 医疗保健中的人工智能
背景情况:
- 慢性阻塞性肺病 (COPD) 恶化由于其不可预测性和严重的患者影响,给医疗保健带来了重大挑战.
- 现有的COPD预测模型往往缺乏实时功能,无法整合多源数据进行准确的预测.
研究的目的:
- 开发和验证一个网络物理系统 (CPS) 支持的框架,用于实时预测COPD恶化.
- 整合各种数据源,包括临床和在线数据,以提高预测准确度.
- 为了利用先进的机器学习技术进行可靠的COPD恶化预测.
主要方法:
- 实施一个网络物理系统 (CPS) 框架,整合初级 (临床) 和二级 (在线) 数据.
- 机器学习算法的应用,包括随机森林和人工神经网络,用于特征选择和预测.
- 使用ANOVA进行统计验证,以协调各种数据源并确保模型的稳定性.
主要成果:
- 拟议的框架在实时预测COPD恶化方面表现出有效性.
- 关键的性能指标包括准确性,精度,回忆,F1得分和AUC表明了强大的预测潜力.
- 该系统有助于早期发现COPD恶化.
结论:
- 支持CPS的框架提供了一种主动的方法来管理COPD恶化.
- 该系统支持及时发出警报,准确预测和明智的临床决策.
- 这种方法有可能改善患者的治疗结果,并降低医疗保健支出.
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