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通过机器学习与人口药理动力学相结合,实时预测老年患者利康纳的血度模型
Ruixiang Liu1, Pan Ma1, Dongxin Chen2
1Department of Pharmacy, the First Affiliated Hospital of Army Medical University, Chongqing, People's Republic of China.
机器学习模型通过纳入药理动力学参数,准确预测老年患者的伏利可纳 (VCZ) 血度. 这种方法有助于个性化剂量,提高治疗效率和安全性.
科学领域:
- 药理学 药理学是指药理学的学科.
- 计算生物学 计算生物学
- 老年病的医生 老年病的医生
背景情况:
- 沃里康纳 (VCZ) 是一种具有狭窄治疗指数的关键抗真菌药物.
- 老年患者由于与年龄相关的生理变化和肝脏新陈代谢,VCZ血度的变化增加.
- 准确预测VCZ水平对于优化老年人群治疗至关重要.
研究的目的:
- 开发和验证一种机器学习 (ML) 模型,用于预测老年患者的伏利可纳 (VCZ) 度.
- 研究药物动力学参数对ML模型对VCZ水平的预测准确性的影响.
- 在老年患者中建立个性化VCZ剂量策略的基础.
主要方法:
- 一项回顾性研究利用了31个特征,包括人群药理动力学 (PPK) 参数.
- 使用递归特征消除与交叉验证 (RFECV) 进行了特征选择.
- 结合XGBoost,随机森林 (RF) 和CatBoost的整体ML模型被开发并使用SHAP进行解释.
主要成果:
- 纳入药理动力学参数显著提高了ML模型的预测性能.
- 最终的组合模型获得了0.828的R2,特征选择可以减少复杂性而不会损失性能.
- 外部验证证明了强大的预测能力,R2为0.633,MAE为1.094,MSE为2.286.
结论:
- 这项研究提出了第一个集成药理学参数的ML模型,用于预测老年患者的VCZ血度.
- 通过特征选择来改进的优化模型,为个性化沃里康纳剂量提供了潜在的参考.
- 这些发现旨在提高老年人群中沃里可纳治疗的疗效和安全性.
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