使用状态空间模型和基于机器学习的框架监测多阶段医疗保健过程
Ali Yeganeh1, Arne Johannssen1, Nataliya Chukhrova2
1University of Hamburg, 20146 Hamburg, Germany.
Artificial intelligence in medicine
|April 5, 2024
概括
本研究介绍了一种用于多阶段医疗保健过程的智能控制图表方法. 它增强了异常的早期检测,提高了患者的安全和外科手术的结果.
科学领域:
- 医疗保健管理的管理
- 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:
- 像手术结果一样,对医疗保健过程的有效监测对于早期检测变化和确保患者安全至关重要.
- 统计过程监测 (SPM) 工具,特别是控制图表,通常用于治疗过程监测.
- 医疗保健过程往往涉及多个阶段,需要多阶段过程监测 (MPM) 技术来进行全面的监督.
研究的目的:
- 通过开发新的MPM控制图表来解决MPM在医疗保健中的有限应用.
- 加强在医疗保健过程中早期发现和确认非自然条件.
- 通过先进的过程监控来提高患者安全.
主要方法:
- 专门为医疗保健数据设计的多阶段过程监控 (MPM) 控制图的开发.
- 整合机器学习技术与统计控制图表,称为智能控制图表 (ICC),以提高检测能力.
- 通过蒙特卡洛模拟进行验证,并应用于现实世界甲状腺癌手术数据.
主要成果:
- 与传统的统计控制图表相比,拟议的ICC方法在模拟中表现出更高的性能.
- 双阶段甲状腺癌手术的现实实例研究有效地说明了开发的MPM控制图的实际实用性.
- 该框架有助于更早,更准确地识别过程偏差.
结论:
- 开发的智能控制图表框架为监控复杂的多阶段医疗保健过程提供了重大进展.
- 这种方法提高了异常的早期检测,有助于提高患者安全和医疗保健质量.
- 该研究强调了将MPM和机器学习整合到临床环境中的潜力.
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