在纵向药理动力学/药理动力学分析中探索变压器模型,并与替代自然语言处理模型进行比较
Yiming Cheng1, Hongxiang Hu1, Xin Dong1
1Clinical Pharmacology, Pharmacometrics, Disposition & Bioanalysis, Bristol Myers Squibb, 556 Morris Avenue, Summit, NJ 07901, United States.
本研究比较了自然语言处理 (NLP) 模型用于药理动力学/药理动力学 (PK/PD) 分析. 集成即使是有限的未见数据也能显著提高可见和未见PK/PD数据的模型预测准确度.
科学领域:
- 制药指标 (Pharmacometrics) 是一个指标.
- 量化药理学 量化药理学
- 计算生物学 计算生物学
背景情况:
- 机器学习的进步激发了人们对将这些技术应用于定量药理学的兴趣.
- 纵向药理动力学/药理动力学 (PK/PD) 建模需要强大的分析方法来解释复杂的时间序列数据.
- 需要对PK/PD分析的各种自然语言处理 (NLP) 算法进行全面评估.
研究的目的:
- 为了研究变压器模型在纵向PK/PD数据分析中的应用.
- 为了比较不同NLP模型的性能,包括LSTM和神经ODE,用于PK/PD建模.
- 评估模型在预测可见和不可见PK/PD疗法方面的性能.
主要方法:
- 在三个不同的剂量方案中利用虚拟的PK/PD数据.
- 比较了变压器,长期短期记忆 (LSTM) 和神经ODE模型的预测性能.
- 评估了包括培训 (可见) 和排除培训 (不可见) 方案的模型准确性.
主要成果:
- 对于可见的方案,LSTM和神经ODE表现强,而对于未见的方案,信息损失较小.
- 变压器模型,类似于神经ODE,在描述时间序列PK/PD数据方面表现出色,但在精确推断到未见的方案方面遇到了困难.
- 将少量未见的数据纳入培训套件,大大提高了所有测试方案的预测性能.
结论:
- 这项研究开创了用于时间序列PK/PD分析的变压器模型的使用.
- 一个系统的比较揭示了目前在PK/PD中的NLP模型的优点和局限性.
- 即使使用最小的未见数据来增强数据,也是提高PK/PD建模中的预测准确度的关键策略.
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