在使用机器学习的成年患者中预测伏利可纳的血最低度
Lin Cheng1, Yue Zhao1, Zaiming Liang1
1Department of Pharmacy, the First Affiliated Hospital of Army Medical University (Third Military Medical University), Gao Tanyan Street 29#, Sha Pingba, Chongqing 400038, PR China.
概括
机器学习模型准确地预测了有毒的伏利可纳 (VCZ) 度. 专蛋白和年龄等关键因素影响预测,有助于调整剂量以获得更好的患者结果.
科学领域:
- 药理动力学和药理动力学
- 计算生物学 计算生物学
- 临床药理学 临床药理学
背景情况:
- 沃里康纳 (VCZ) 血最低度对于疗效和毒性至关重要.
- 非线性药理动力学使预测VCZ度与毒性关系变得复杂.
- 需要准确预测有毒VCZ水平.
研究的目的:
- 开发一种基于机器学习 (ML) 的方法来预测利康纳的毒性血最低度 (>5μg/mL).
- 确定影响伏利康纳毒性的关键临床因素.
主要方法:
- 使用1318伏利康纳的血度进行了回顾性研究.
- 采用随机森林 (RF),梯度增强 (GB) 和极端梯度增强 (XGBoost) 的ML算法.
- 使用了SHapley添加剂扩展 (SHAP) 进行变量重要性和外部验证.
主要成果:
- 开发了9个具有良好表现的分类模型 (平均BA 0.704,ACC 0.788).
- XGBoost模型的表现优于GB和RF;年轻的成年模型显示出更好的预测能力.
- 确定了关键预测因素:白蛋白,总胆酸,血小板,年龄和炎症标志物.
结论:
- ML模型提供了一种可靠的方法来预测有毒沃里康纳暴露.
- SHAP分析为优化伏利康纳剂量调整提供了宝贵的见解.
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