机器学习方法中的时间和时间性,以改善癌症临床决策支持:文献综述
Yiyu Wang1, Anastasia Griva2, Umair Ul Hassan3
1J.E. Cairnes School of Business and Economics, University of Galway, University Road, Galway H91 TK33, Ireland.
International journal of medical informatics
|December 13, 2025
概括
本次审查发现,虽然癌症临床决策支持系统 (CDSS) 中的机器学习 (ML) 模型越来越多地使用时间数据进行预后,但它们往往忽视了患者的经验和纵向数据集成,以提供更好的决策支持.
科学领域:
- 机器学习在瘤学中
- 临床决策支持系统 临床决策支持系统
- 时间数据分析时间数据分析
背景情况:
- 机器学习 (ML) 越来越多地用于癌症护理的临床决策支持系统 (CDSS).
- 纳入时间和时间相关的维度对于准确的癌症决策支持至关重要.
研究的目的:
- 系统地审查如何将时间维度集成到癌症CDSS的ML模型中.
- 确定基于ML的癌症决策支持时间建模的趋势,局限性和未来方向.
主要方法:
- 按照PRISMA指南进行系统的文献审查.
- 搜索了Web of Science的数据库,从2014年到2023年的研究.
- 分析了83项使用安科纳时间框架进行时间方面分类的研究.
主要成果:
- 自2020年以来,癌症CDSS的时间ML的研究活动显著增长.
- 常见的应用包括生存分析和预后的时间到事件预测.
- 尚未探索的领域包括纵向数据,生物时间和患者/临床医生的时间经验.
结论:
- 癌症CDSS的时间ML进展明显,但局限性仍然存在.
- 未来的研究应该专注于强大的纵向建模和整合时间的临床/人类方面.
- 加强时间数据集成是提高决策支持准确性和相关性的关键.
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