机器学习技术在人口药理动力学/药理动力学建模中的应用
Mizuki Uno1, Yuta Nakamaru1, Fumiyoshi Yamashita1
1Department of Drug Delivery Research, Graduate School of Pharmaceutical Sciences, Kyoto University, 46-29 Yoshidashimoadachi-cho, Sakyo-ku, Kyoto, 606-8501, Japan.
机器学习通过发现受试者数据中的复杂模式来增强人口药理动力学/药理动力学 (pop-PK/PD) 分析. 本综述探讨了用于改进PK/PD建模和共变量识别的机器学习应用.
科学领域:
- 药理学 药理学是指药理学的学科.
- 生物统计学 生物统计学
- 计算生物学 计算生物学
背景情况:
- 群体药理动力学/药理动力学 (pop-PK/PD) 集成来自多个受试者的数据以评估变异性.
- 传统的pop-PK/PD分析包括模型结构的确定,错误建模,共变量分析和验证.
- 机器学习 (ML) 为复杂模式识别提供了先进的数据驱动方法.
研究的目的:
- 审查机器学习技术在pop-PK/PD研究中的多样化应用.
- 突出ML在处理复杂关系方面对传统方法的优势.
- 展示ML在pop-PK/PD中的共变量查和预测建模中的实用性.
主要方法:
- 在pop-PK/PD中对机器学习应用程序的现有文献的审查.
- 讨论与PK/PD数据分析相关的ML算法.
- 探索ML在模型构建和共变量识别中的作用.
主要成果:
- 机器学习擅长识别PK/PD数据中的复杂,非线性关系.
- 机器学习算法在预先选共变量方面表现出强大的能力.
- ML有助于在pop-PK/PD中开发可靠的预测模型.
结论:
- 机器学习代表了一个强大而不断发展的工具集,用于推进Pop-PK/PD研究.
- 机器学习的整合可以导致更准确和个性化的治疗策略.
- 采用ML技术对于PK/PD分析的未来创新至关重要.
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