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机器学习模型的开发和内部验证,用于对脊髓质瘤患者个性化的生存预测

Mert Karabacak¹, Alexander J Schupper¹, Matthew T Carr¹

¹Department of Neurosurgery, Mount Sinai Health System, 1468 Madison Ave, New York, NY, USA.

The spine journal : official journal of the North American Spine Society

|February 16, 2024

概括

机器学习模型准确地预测脊髓质瘤 (SCG) 患者的生存率. 在线计算器可用于帮助医生评估个体患者的预后.

科学领域:

机器学习在瘤学中的应用.
预测模型用于神经瘤学.
对罕见瘤的生存分析.

背景情况:

脊髓质瘤 (SCG) 的存活率受到许多因素的影响.
准确的预后模型对于SCG患者管理至关重要.
识别SCG生存的关键决定因素对于个性化医学至关重要.

研究的目的:

开发可解释的机器学习 (ML) 模型,以预测SCG患者的生存结果.
创建一个可访问的在线计算器,整合这些ML模型用于临床使用.
通过精确的个人生存预测,增强临床决策.

主要方法:

使用国家癌症数据库 (NCDB) (2010-2019) 进行回顾性,基于人口的队列研究.
使用了五个ML算法 (TabPFN,CatBoost,XGBoost,LightGBM,Random Forest),并进行了超参数优化.
使用ROC曲线,PRCs,AUROC,AUPRC和Brier Score评估模型性能;使用SHAP和PDP进行解释.

主要成果:

顶级的ML模型实现了高的AUROC:0.938 (1年),0.907 (3年) 和0.902 (5年).
确定的主要预测因素包括组织学,瘤等级,年龄,手术,放射治疗和瘤大小.
该研究包括数千名SCG患者,诊断后1,3年和5年的死亡率各不相同.

结论:

ML模型显示了SCG患者预后的高准确性和区分能力.
一个交互式在线计算器可供医生使用,增强临床适用性.
这种可解释的ML方法支持精准医学和SCG患者护理的未来研究.

关键词:

人工智能的人工智能是人工智能.机器学习是机器学习.在线计算器在线计算器个性化医疗是个性化的医疗.精确瘤学是一门精确的专业.预测模型的预测建模.脊髓质瘤是脊髓质瘤的一种.预测生存的预测.

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