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机器学习模型用于预测结直肠癌患者的化疗诱导药物不良反应

Yingfei Wu¹, Wei Zhao², Linxiao Zhang¹

¹Department of Gastroenterology, The Second Hospital of Shandong University, Jinan, Shandong 250033, China.

Digestive and liver disease : official journal of the Italian Society of Gastroenterology and the Italian Association for the Study of the Liver

|July 6, 2025

概括

机器学习模型准确地预测了结直肠癌患者的化疗副作用,改进了传统方法. 这些工具增强了针对个性化癌症治疗的临床决策.

科学领域:

在瘤学瘤学.
计算生物学计算生物学
药理学药理学是指药理学的学科.

背景情况:

化疗诱导的药物不良反应 (ADR) 在结直肠癌患者中很常见.
开发了机器学习模型来预测这些ADR,利用复杂的数据关系.

研究的目的:

开发和评估机器学习模型,用于预测结直肠癌中化疗诱导的副作用.
将这些模型的预测精度与传统方法进行比较.

主要方法:

利用了11个变量,包括患者的人口统计,病史和治疗细节.
评估了四种模型:随机森林,支持向量机,XGBoost和LASSO回归.
在4072个化疗周期中预测了6个特定的ADR和整体的ADR.

主要成果:

随机森林在预测整体ADR方面取得了最高的准确性 (0.8779).
XGBoost (0.8758) 和支持矢量机 (0.8377) 也表现出强大的预测性能.
模型显示,与整体ADR相比,单个ADR的预测优越,随机森林优异.

结论:

开发的机器学习模型有效地预测化学疗法的副作用,并且具有很高的准确性.
这些模型提供了更好的概括性,并帮助临床医生为个性化医学做出明智的决策.
这些发现支持将先进的计算方法整合到癌症患者管理中.

关键词:

结肠直肠癌是一种癌症.药物诱导的副作用药物诱导的副作用机器学习机器学习预测模型的预测模型.

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