用于识别与癌症相关的社会人口学因素的机器学习技术:系统文献综述
Liz González-Infante1,2, Gaston Marquez2,3, Solange Parra-Soto2,4
1Facultad de Ciencias Empresariales, Universidad del Bío-Bío, Andrés Bello 720, Chillán, Chile, 56 422463324.
Journal of medical Internet research
|January 28, 2026
概括
机器学习 (ML) 显示出对理解与社会因素相关的癌症差异的希望. 然而,将社会人口统计数据整合到用于癌症护理的ML模型中,需要更多的研究来获得公平可靠的应用.
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:
- 癌症是全球主要的死亡原因,发病率不断上升.
- 机器学习 (ML) 越来越多地被用于癌症预测,诊断和治疗.
- 对于健康公平至关重要的社会人口统计变量在瘤学ML模型中未得到充分利用.
研究的目的:
- 审查ML在识别社会人口统计因素和癌症结果之间的关联方面的应用.
- 绘制ML算法,变量和该领域的验证当前研究的地图.
主要方法:
- 按照PRISMA指南进行系统的文献审查.
- 在6个数据库中使用PICO框架对ML,社会人口统计和癌症研究进行搜索.
- 纳入标准和对所选研究的方法质量评估.
主要成果:
- 19项研究符合328个记录中的标准.
- 监督的ML技术,特别是随机森林和极端梯度增强,是常见的.
- 关键变量包括年龄,性别,教育,收入和地理位置;交叉验证是主要的评估方法.
结论:
- ML可以揭示癌症社会决定因素的模式,但研究是分散的.
- 未来的工作需要整合上下文因素,提高模型透明度,加强外部验证.
- 增强的ML模型对于公平,可泛化和可操作的癌症护理至关重要.
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