在结核患者中预测药物诱导肝损伤的可解释机器学习:模型开发和验证研究
Yue Xiao1, Yanfei Chen1, Ruijian Huang1
1School of International Pharmaceutical Business, China Pharmaceutical University, Nanjing, Jiangsu, China.
BMC medical research methodology
|April 20, 2024
概括
这项研究开发了一种可解释的预测模型,用于结核病 (TB) 患者的药物诱导肝损伤 (DILI). XGBoost模型显示出优异的性能,识别了DILI的关键风险因素.
科学领域:
- 医疗信息学 医疗信息学
- 医疗保健中的机器学习
- 药物监督 药物监督 药物监督
背景情况:
- 药物诱导性肝损伤 (DILI) 是结核病 (TB) 治疗期间的一个重大问题.
- 需要预测模型来识别患有DILI高风险的患者.
研究的目的:
- 开发和验证可解释的TB患者DILI预测模型.
- 为了比较XGBoost,随机森林和LASSO物流模型的性能.
主要方法:
- 利用了来自宁波市的7071名结核病患者的数据集.
- 开发并比较了XGBoost,RF和LASSO物流模型.
- 采用SHAP用于模型解释性和确定关键预测变量.
主要成果:
- 该XGBoost模型实现了卓越的性能,其AUROC为0.89和AUPR为0.75.
- DILI的主要预测因素包括先前存在的肝病 (DIH,FLD),年龄,ALT和Tbil.
- SHAP分析阐明了这些变量对DILI预测的贡献.
结论:
- 与RF和LASSO相比,XGBoost在结核病患者中提供了更好的DILI预测准确度.
- SHAP提高了DILI预测模型的临床解释性和适用性.
- 进一步的研究应该集中在外部验证和功能集成上.
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