偏见还是最适合? 在单一模型机器学习中对SEER和NCDB数据集进行比较分析,以预测骨髓瘤生存结果
Andrew G Girgis1,2,3, Bishoy M Galoaa1,2,4, Megan H Goh1,2
1Orthopaedic Oncology Service, Department of Orthopaedic Surgery, Massachusetts General Hospital, Boston, MA, USA.
Clinical orthopaedics and related research
|October 7, 2025
概括
机器学习模型用于骨髓瘤生存预测,在内部表现良好,但在SEER和NCDB等不同数据库中应用时表现不佳. 这表明模型学习数据库特定的模式,限制了无需重新验证的概括性和临床实用性.
科学领域:
- 整形瘤学 整形瘤学
- 机器学习在医学中的应用
- 癌症预后 癌症预后
背景情况:
- 机器学习模型在骨科瘤学中越来越多地用于预测骨髓瘤生存结果.
- 模型通常在单个数据集 (SEER,NCDB) 上进行训练,这可能导致数据库特定的模式和有限的临床实用性.
研究的目的:
- 对比SEER和NCDB训练模型对2年和5年骨髓瘤存活率预测的准确性.
- 通过外部验证在一个数据库上训练的模型,使用来自另一个数据库的数据来评估模型的通用性.
- 确定影响预测准确性的关键因素.
主要方法:
- 使用SEER (n=4278) 和NCDB (n=4049) 的骨髓瘤患者数据 (2000-2018/2004-2018) 开发了单独的机器学习模型.
- 模型在各自的数据集上进行内部验证,并在另一个数据库上进行外部验证.
- 使用准确度,AUC,布莱尔得分,精度,回忆和F1得分来评估性能.
主要成果:
- 在2年和5年内,内部验证显示NCDB (AUC 0.93/0.91) 和SEER (AUC 0.90/0.92) 模型的表现非常出色.
- 外部验证显示可转移性较差,AUC显著下降 (NCDB在SEER上为0.67/0.60;SEER在NCDB上为0.61/0.56).
- NCDB模型优先考虑治疗变量,而SEER模型强调人口统计学,解释跨数据库性能失败.
结论:
- 机器学习模型必须在将其应用的同一个数据库环境中进行验证.
- 在SEER和NCDB数据上训练的模型学习数据库特定的模式,而不是可概括的疾病模式.
- 骨髓瘤生存模型的交叉数据库应用在没有重新验证的情况下是不可靠的.
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