对于脊髓转移患者的经典和机器学习生成的生存预测模型的比较 - - 两种最近开发的算法的元分析
Hung-Kuan Yen1,2,3, Wei-Hsin Lin1, Olivier Quinten Groot4
1Department of Orthopedic Surgery, National Taiwan University Hospital, Taipei, Taiwan.
Global spine journal
|July 29, 2024
概括
骨瘤学研究小组 (SORG) 的经典算法 (CA) 在非美国队列中表现出比机器学习算法 (MLA) 的性能变化较小. 整合特定区域的数据对于可概括的预测模型至关重要.
科学领域:
- 整形瘤学 整形瘤学
- 医疗信息学 医疗信息学
- 生物统计学 生物统计学
背景情况:
- 骨瘤学研究小组 (SORG) 开发了经典算法 (CA) 和机器学习算法 (MLA) 来预测骨相关事件.
- 评估这些算法在不同地理区域的通用性和性能变异性对于临床应用至关重要.
研究的目的:
- 使用元分析来比较SORG CA和SORG MLA的歧视能力.
- 测试SORG CA是否在非美国验证队列中表现出比SORG MLA更少的性能变化,假设其排除BMI等区域变量有助于此.
主要方法:
- 进行了系统性审查和元分析,以汇集SORG CA和SORG MLA的歧视能力 (曲线下的面积 - AUC).
- 分析使用了AUC的逻辑转换,直接比较了logit (AUC) 值.
- 进行了子组分析,以比较美国和非美国队列之间的算法性能.
主要成果:
- 聚合的logit (AUC) 值表明SORG CA和SORG MLA在90天和1年间隔的性能不同.
- 与台湾相比,所有算法在美国表现优越 (P < .001).
- 与SORG MLA相比,SORG CA的表现受到非美国人队伍的影响较小.
结论:
- 研究结果表明,SORG CA在不同地区比SORG MLA更强大.
- 该研究强调了将特定区域的变量纳入预测模型的重要性,以提高它们对不同人群的概括性.
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