在医疗保健中使用模拟和机器学习进行混合建模.
Ali Ahmadi1, Masoud Fakhimi1, Carin Magnusson2
1Surrey Business School, University of Surrey, Guildford, UK.
概括
混合建模与模拟 (M&S) 和机器学习 (ML) 方法通过将模拟与数据驱动学习相结合来增强医疗保健. 本综述综合了M&S-ML在医疗保健决策中的趋势和机会.
科学领域:
- 医疗保健信息学 医疗保健信息学
- 计算科学 计算科学
- 人工智能的人工智能
背景情况:
- 建模与模拟 (M&S) 和机器学习 (ML) 正在迅速发展.
- 将M&S和ML集成到混合M&S-ML中,可以提高精度,效率和决策支持.
- 医疗保健应用越来越受益于这些综合方法.
研究的目的:
- 在医疗保健中审查最先进的混合M&S-ML.
- 综合方法,工具,集成模式和应用程序.
- 确定M&S-ML在应对医疗保健挑战方面的趋势和机会.
主要方法:
- 对90项相关研究进行了系统审查.
- 在混合方法中使用的M&S和ML方法的分析.
- 检查软件,编程语言,集成模式和数据交换.
- 应用领域,类型和混合化动机的分类.
主要成果:
- 确定了用于医疗保健的混合M&S-ML的突出方法和技术趋势.
- 突出了将M&S与ML结合起来解决医疗保健问题的机会.
- 综合了M&S-ML集成的当前实践和未来方向.
结论:
- 混合M&S-ML方法显示出促进医疗保健的巨大潜力.
- 这些方法可以改善方法的创新,并支持基于证据的决策.
- 进一步发展M&S-ML对于应对复杂的医疗保健挑战至关重要.
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