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使用机器学习来预测膀癌生存结果:一个系统的文献综述

Yi-Shao Liu¹, Ryan Thaliffdeen¹, Sola Han¹

¹College of Pharmacy, The University of Texas at Austin, 2409 University Ave, Austin, TX, USA.

Expert review of pharmacoeconomics & outcomes research

|June 12, 2023

概括

机器学习 (ML) 显示了预测膀癌存活率的潜力,但数据质量和特征选择需要改进,以获得强大的模型. 需要进一步的研究来增强ML在膀癌症治疗中的应用.

科学领域:

在瘤学瘤学.
医疗信息学医疗信息学
数据科学数据科学数据科学

背景情况:

在全球范围内,膀癌仍然是一个重大的健康问题.
准确预测整体存活率 (OS) 对于有效的患者管理和治疗计划至关重要.
机器学习 (ML) 提供了提高瘤学预后准确性的潜力.

研究的目的:

系统地审查和总结机器学习 (ML) 算法的应用,用于预测膀癌患者的整体存活率 (OS).
在现有研究中识别常见的ML技术,数据特征和方法论挑战.

主要方法:

在PubMed和Web of Science进行了系统的文献搜索,直到2022年2月,使用与膀癌,ML和死亡率相关的关键词进行了系统的文献搜索.
纳入标准侧重于使用患者级数据的研究,不包括基因表达研究. 通过IJMEDI检查清单来评估研究质量.

主要成果:

包括14项研究,人工神经网络和物流回归是最常见的ML算法.
关键特征包括患者的年龄,性别,瘤阶段和等级. 数据处理和特征选择在不同研究中差异很大.
大多数研究质量均为中等,突出需要改进数据准备和部署描述.

结论:

通过改进的OS预测,ML显示了改善膀癌症护理的前景.
处理数据处理,特征选择和数据质量对于开发可靠的ML模型至关重要.
本次审查强调了对基于ML的膀癌预后工具的标准化方法和改进的解释能力的需要.

关键词:

膀癌:膀癌是一种癌症.机器学习是机器学习.瘤学在瘤学方面.生存结果的结果.系统性审查系统性审查

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