机器学习方法的评估,以从化疗数据追溯检测卵巢癌复发
A D Coles1, C D McInerney2, K Zucker3,4
1School of Computing, University of Leeds, Leeds, UK.
概括
机器学习模型可以从电子健康记录中准确识别癌症复发日期,提高研究效率. 随机森林和决策树在检测上皮卵巢癌进展方面表现出很高的表现.
科学领域:
- 在瘤学瘤学.
- 医疗信息学 医疗信息学
- 机器学习 机器学习
背景情况:
- 在全球范围内,癌症复发数据在电子健康记录中被捕捉得很差.
- 手动检查患者病历以检查复发是低效和昂贵的.
- 机器学习 (ML) 为加快癌症研究的健康记录审查提供了一个潜在的解决方案.
研究的目的:
- 评估ML模型来识别上皮卵巢癌复发/进展日期.
- 评估随机森林,条件推理树,决策树和后勤回归的有效性.
- 提高临床数据中癌症复发检测的准确性和效率.
主要方法:
- 利用了利兹教学医院信托中心531名患者的化疗数据.
- 应用了四种ML模型:随机森林,条件推理树,决策树和后勤回归.
- 确定了癌症复发/进展和复发患者的代理日期.
主要成果:
- 随机森林模型在识别复发事件方面获得了高F1分数0.941 .
- 所有测试的模型都显示F1分数>0.940用于识别癌症复发/进展的患者.
- 随机森林和决策树模型分类与图表审查的时间与下一次处理保持一致.
结论:
- ML模型可以有效地识别复发/进展日期和受影响的患者.
- 随机森林和决策树模型显示了相似的性能.
- 模型选择应考虑图表审查援助的解释性需求和实施方便性.
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