通过平衡不平衡的队列来改善医疗保健中的生存模型:一种新的方法
Catherine Ning1, Dimitris Bertsimas1, Per Eystein Lønning2
1Operations Research Center, Massachusetts Institute of Technology, Cambridge, MA.
JCO clinical cancer informatics
|January 28, 2026
概括
改善癌症患者的生存模型包括重新平衡培训数据,以更好地代表高风险和低风险群体. 这种风险分层抽样提高了整个预后谱的模型可靠性,有助于临床决策.
科学领域:
- * 瘤学 在线咨询
- * 生物统计学
- * 机器学习 * 机器学习
背景情况:
- *生存模型对于预测瘤学中患者的结果至关重要.
- *在代表不足的高风险和低风险患者子组中存在绩效差异.
- *临床决策在这些极端预后区域的影响最大.
研究的目的:
- * 改善在预后小组中代表性不足的生存模型性能.
- *为培训数据重组引入一种新的风险分层抽样方法.
- *为了提高预后谱的尾部层的可靠性.
主要方法:
- * 开发了一种风险分层抽样方法,根据基线预后风险对患者进行分区.
- * 应用了分层内的匹配,以使风险分布中的代表性平等.
- * 考克斯比例危险模型在结直肠肝转移 (CRLM) 队列上进行了内部和外部训练和验证.
主要成果:
- * 风险平衡的队列在内部模型验证中表现出一致的改进.
- * 拟议的方法保持了整体模型校准.
- * 在外部验证中,分层C指数值在代表性不足的高风险和低风险层中得到改善.
结论:
- *有针对性的训练数据再平衡改善了瘤学中的生存模型性能.
- * 风险分层抽样方法是实用的,并且不依赖于模型.
- * 这种方法对于要求在整个风险连续性中具有预测可靠性的应用至关重要.
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