一个概念框架来预测慢性病患者的疾病进展,使用机器学习和过程采矿
Nichalini Kandasamy1, Thierry Chaussalet1, Artie Basukoski1
1School of Computer Science & Engineering, University of Westminster, London, UK.
Studies in health technology and informatics
|June 30, 2023
概括
本研究介绍了一个框架,将过程挖掘和机器学习结合起来,用于医疗保健. 这种整合旨在利用先进的数据科学技术增强医疗保健过程的分析.
科学领域:
- 数据科学数据科学数据科学
- 人工智能的人工智能
- 医疗保健信息学 医疗保健信息学
背景情况:
- 过程挖掘和机器学习是已建立的技术,在医疗保健中具有单独的应用.
- 在医疗保健中使用过程挖掘和机器学习是新兴的研究领域.
- 现有的文献广泛涵盖了个别应用,但缺乏综合框架.
研究的目的:
- 为在医疗保健中同时应用过程挖掘和机器学习提出一个可行的框架.
- 探索整合这两种强大的分析技术的协同潜力.
- 推进医疗保健过程的数据驱动分析.
主要方法:
- 开发一个新的框架,集成流程挖掘和机器学习算法.
- 使用过程日志和患者数据进行分析.
- 将机器学习模型应用于从过程挖掘中获得的见解.
主要成果:
- 拟议的框架证明了将过程挖掘和机器学习结合起来是可行的.
- 增强对医疗保健工作流程的理解和优化潜力.
- 确定了改善患者护理和运营效率的机会.
结论:
- 过程挖掘和机器学习的整合为医疗分析提供了显著的优势.
- 拟议的框架为未来的研究和实际实施提供了基础.
- 这种综合方法可以带来更智能,更有效的医疗保健系统.
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