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早期肝癌的并发症模式中的种族差异:机器学习分析

Bingya Ma¹, Kai Zheng^2,3, Fa-Chyi Lee⁴

¹Department of Epidemiology and Biostatistics, University of California Irvine, Irvine, CA, USA.

Cancer control : journal of the Moffitt Cancer Center

|July 30, 2025

概括

早期发病的肝癌风险因种族而异,具有不同的并发症模式. 机器学习模型揭示了显著的种族差异,有助于针对性预防肝癌.

科学领域:

在瘤学瘤学.
遗传学和基因组学遗传学和基因组学
公共卫生公共卫生

背景情况:

早期发病的肝癌 (EOLC) 的发病率在全球范围内正在上升,但病因学的理解是有限的,特别是在非亚洲人群中.
现有的研究往往忽略了在不同种族和民族群体中为EOLC做出贡献的特定并发症概况.

研究的目的:

调查不同种族和种族群体中与EOLC相关的并发症模式.
开发和评估特定于种族/种族的机器学习 (ML) 模型,用于预测EOLC风险.
在不同人群中确定影响EOLC风险的关键并发症.

主要方法:

一项利用加利福尼亚大学健康数据仓库的病例对照研究,包括1574名EOLC患者 (18-49岁) 和7870名对照.
应用多种ML分类方法 (决策树,随机森林,逻辑回归,XGBoost,LightGBM) 来根据人口统计和并发症预测肝癌风险.
使用F1分数和SHapley添加式扩展 (SHAP) 来评估模型性能,以确定每个种族群体有影响力的并发症.

主要成果:

在并发症患病率方面观察到显著的种族差异:亚洲和太平洋岛民 (API) 呈现较高的乙型肝炎病毒 (HBV) 感染率;西班牙裔人患肝硬化,高血压,糖尿病和乙型肝炎病毒 (HCV) 感染率较高;白人表现出更高的焦虑,喘,甲状腺功能低下和胆管炎.
种族/种族特定的ML模型表现出不同的预测性能:API (F1=0.77,AUC=0.90) 和西班牙裔 (F1=0.77,AUC=0.92) 模型的表现优于白人模型 (F1=0.64,AUC=0.87).
在SHAP分析中,HBV被确定为API的主要并发症,而HCV和代谢障碍对西班牙裔人来说是显著的. 白人人口表现出更分散的共同疾病模式.

结论:

这项研究强调了EOLC并发症模式中的显著种族差异.
机器学习模型可以通过分析这些独特的模式,有效地识别高风险人群.
研究结果支持针对特定种族和民族群体量身定制的针对性预防策略的制定.

关键词:

这就是 SHAP SHAP 的意思.伴随性疾病发生率.肝癌肝癌是一种肝癌.机器学习是机器学习.种族差异是种族的差异.

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