用ChatGPT大语言模型对药理动力学数据分析的快速工程策略的评估
Euibeom Shin1, Murali Ramanathan2
1Department of Pharmaceutical Sciences, University at Buffalo, The State University of New York, 355 Pharmacy, Buffalo, NY, 14214-8033, USA.
聊天GPT对制药动力学数据分析任务 (如编写和编码) 有希望. 然而,它的数值计算准确性需要改进,以便在PK和药理学中可靠地使用.
科学领域:
- 药理动力学 药理动力学
- 制药指标 (Pharmacometrics) 是一个指标.
- 计算生物学 计算生物学
- 人工智能在药物开发中的作用
背景情况:
- 像ChatGPT这样的大型语言模型 (LLM) 提供了自动化科学任务的潜力.
- 药理动力学 (PK) 数据分析涉及复杂的计算和报告.
研究的目的:
- 系统地评估ChatGPT在各种PK数据分析任务中的表现.
- 确定ChatGPT在PK应用中的优点和局限性.
主要方法:
- 聊天GPT在报告编写,用于图形和计算 (AUC,AUMC) 的 R 代码生成以及解决 PK 字问题方面进行了测试.
- 研究了快速工程策略 (思维链,问题分离).
主要成果:
- 在报告撰写和R代码生成方面,ChatGPT表现令人满意.
- 提供了关于PK原理的准确信息,但在数值计算中出现了较高的错误率,特别是指数函数.
- 输出无法重现,即时工程只能部分减少错误.
结论:
- 聊天GPT具有作为PK数据分析的生产力工具的潜力,特别是在编写和编码方面.
- 在PK和药量计学中,可靠的使用受到数值计算中的不准确性所阻碍,需要进一步开发.
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