使用通用添加模型和自动编码器监测医疗保健过程的监测框架
Ali Yeganeh1, Arne Johannssen1, Nataliya Chukhrova1
1University of Hamburg, 20146 Hamburg, Germany.
Artificial intelligence in medicine
|December 2, 2023
概括
本研究引入了一种新的统计过程监测 (SPM) 框架,使用通用添加模型 (GAM) 和用于医疗保健的机器学习. 它有效地监控复杂的患者数据,比传统方法更好地检测.
科学领域:
- 医疗保健服务研究 医疗服务研究
- 统计过程监测 统计过程监测
- 医疗保健中的机器学习
背景情况:
- 有效监测医疗保健流程对于质量改善至关重要.
- 风险调整的控制图表是一个有希望的统计过程监测 (SPM) 方法.
- 现有的方法经常使用后勤模型,限制分析复杂的健康数据与混合风险因素.
研究的目的:
- 通过通用添加模型 (GAM) 引入用于医疗保健过程监测的新型SPM框架.
- 开发一种基于机器学习的风险调整控制图,用于分析纵向健康数据.
- 评估中风类型和解释变量之间的关系在时间上的稳定性.
主要方法:
- 为风险调整的控制图表实施通用添加模型 (GAM).
- 开发和应用基于机器学习的控制图表.
- 对包括缺血性和出血性中风类型在内的数据集的分析.
- 使用具有参数和非参数风险因素的纵向患者数据.
主要成果:
- 拟议的基于GAM的SPM框架显示了增强的灵活性和检测能力.
- 机器学习驱动的控制图有效地监控复杂的医疗保健数据中关系的稳定性.
- 基于真实急性中风患者数据的模拟验证了该方法的性能.
结论:
- 新的SPM框架为监测复杂的医疗保健流程提供了更强大,更灵活的方法.
- 这种方法通过适应复杂的风险因素相互作用来改进传统技术.
- 该研究强调了GAM和机器学习在推进医疗保健质量监测方面的潜力.
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