开发一个多层次模型,以识别患有延迟开始癌症治疗的风险的患者
Zachary A K Frosch1,2, Jill Hasler3, Elizabeth Handorf2,3
1Department of Hematology/Oncology, Fox Chase Cancer Center, Philadelphia, Pennsylvania.
JAMA network open
|August 14, 2023
概括
机器学习模型可以使用电子健康记录和健康数据的社会决定因素来预测癌症治疗延迟. 需要进一步的研究来改善对脆弱患者群体的预测.
科学领域:
- 在瘤学瘤学.
- 医疗信息学 医疗信息学
- 公共卫生 公共卫生
背景情况:
- 癌症治疗的延迟不成比例地影响弱势群体,影响患者的治疗结果.
- 机器学习 (ML) 有潜力通过将电子健康记录 (EHR) 数据与健康的社会决定因素 (SDOH) 整合来识别有风险的患者.
研究的目的:
- 开发和验证一种ML模型,用于预测癌症治疗延迟超过60天的概率.
- 利用多层次的数据来源,包括临床,人口和社区层面的信息.
主要方法:
- 一项队列研究评估了四种ML方法:LASSO组,贝叶斯增量回归树,梯度增强和随机森林.
- 数据包括乳腺,肺,结直肠,膀或癌 (2013-2019) 诊断的患者的EHR,癌症注册表和美国社区调查信息.
- 模型的性能是使用接收器操作特征曲线 (AUC-ROC) 下的面积来评估的.
主要成果:
- 一个LASSO组模型实现了AUC-ROC为0.713.
- 与延迟可能性较低相关的因素包括治疗机构的诊断,私人保险和缺乏并发症.
- 来自贫困社区的患者观察到延迟的可能性更高;然而,黑人患者和处境不利地区的患者的模型性能较低.
结论:
- 结合EHR和SDOH数据的ML模型可以估计癌症治疗延迟的可能性.
- 改善SDOH数据集成对于提高模型性能至关重要,特别是对于弱势群体.
- 需要进一步的研究来完善预测模型和减轻治疗差异.
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