在临床环境中用于预后和诊断的时间和动态贝叶斯网络:范围审查
João Miguel Alves1, Tiago Martins1, Susana Esteves2
1Faculty of Medicine University of Porto, Department of Community Medicine, Information and Health Decision Sciences, Porto, Portugal; CINTESIS @ RISE - Faculty of Medicine of the University of Porto (FMUP), Porto, Portugal.
Computers in biology and medicine
|October 10, 2025
概括
动态贝叶斯网络 (DBNs) 对模拟时间健康数据,特别是瘤学和重症监护的数据显示出前途. 它们的应用正在扩大,突出了它们在动态的健康环境中对决策的日益增长的临床实用性.
科学领域:
- 医疗信息学 医疗信息学
- 临床数据科学 临床数据科学
- 计算医学是一种计算医学.
背景情况:
- 时间和动态贝叶斯网络 (DBNs) 是分析时间依赖的健康数据的先进统计工具.
- 这些模型允许研究临床变量如何随着时间的推移而变化和相互影响.
研究的目的:
- 对应用贝叶斯网络用于临床环境中的时间建模的文献进行范围审查.
- 绘制现有研究格局的地图,并确定使用时间贝叶斯网络的趋势.
主要方法:
- 在五个主要的电子数据库中进行了全面的文献搜索.
- 确定并分析了利用时间或动态贝叶斯网络用于临床时间关系建模的研究.
主要成果:
- 包括47项研究,自2021年以来出版物的数量大幅增加.
- 预测应用 (74.5%) 比诊断应用 (25.5%) 更常见.
- 瘤学,重症监护和心脏病学是代表性最大的医学专业,标准DBN是最常用的模型类型.
结论:
- DBN显示了跨不同医学领域的时间建模的巨大潜力,特别是在危及生命的疾病中.
- 它们的实用性延伸到长期的病人护理和慢性疾病,表明更广泛的应用.
- 研究兴趣的增加表明DBNs在动态环境中对临床决策支持变得至关重要.
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