将cox回归与机器学习进行比较,用于预测形甲状腺癌的存活率
Lizhen Xu1, Liangchun Cai1,2, Zheng Zhu1
1Shengli Clinical Medical College of Fujian Medical University, 350001, Fuzhou, China.
BMC endocrine disorders
|June 8, 2023
概括
机器学习和考克斯回归有效地预测了无塑性甲状腺癌 (ATC) 的生存率. SHAP值提高了模型的解释性,有助于临床预后,尽管需要外部验证.
科学领域:
- 在瘤学瘤学.
- 生物统计学 生物统计学
- 机器学习在医学中的应用
背景情况:
- 无塑性甲状腺癌 (ATC) 的预后不佳,需要准确的生存预测.
- 传统统计模型和先进的机器学习算法的预测能力的比较对于临床决策至关重要.
研究的目的:
- 评估和比较考克斯回归和各种机器学习算法的预测性能,以评估无塑性甲状腺癌 (ATC) 患者的生存率.
- 评估夏普利添加式解释 (SHAP) 的实用性,用于解释机器学习模型在空中交通中的预测.
主要方法:
- 利用了来自监测,流行病学和最终结果 (SEER) 数据库的数据,用于诊断为ATC的患者.
- 使用考克斯回归和机器学习算法来构建总生存率 (OS) 和癌症特定生存率 (CSS) 的预测模型,分析二进制和时间到事件数据.
- 使用一致性指数 (C指数),布里尔分数和校准曲线评估模型性能,并使用SHAP进行解释性.
主要成果:
- 对于二进制结果,Logistic算法显示出更高的预测准确性 (C指数高达0.811).
- 考克斯回归对时间到事件数据表现良好 (C指数~0.71).
- 在训练集中,DeepSurv算法表现出高性能,但在验证集中表现不佳;用于解释最佳ML模型的SHAP值.
结论:
- 考克斯回归和机器学习模型,加上SHAP的可解释性,在预测ATC患者预后方面表现有前途.
- 研究结果表明,这些模型可以帮助临床实践,但由于样本大小的限制和缺乏外部验证,建议谨慎使用.
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