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使用大型语言模型来选心血管药物开发摘要的实用方面:跨部分研究

Jay G Ronquillo¹, Jamie Ye¹, Donal Gorman²

¹Worldwide Medical and Safety, Pfizer Research and Development, Pfizer Inc, New York, NY, United States.

JMIR medical informatics

|October 4, 2024

概括

评估了三种大型语言模型,以加速心血管药物开发文献查. 它们提供性能,成本,并为高效的数据合成提供快速的工程权衡.

科学领域:

药理学药理学是指药理学的学科.
人工智能的人工智能
生物医学信息学生物医学信息学

背景情况:

心血管药物开发依赖于全面的文献综合.
有效选科学文献对于识别药物指示,机制,生物标志物和结果至关重要.
当前的文献选方法可能耗时且资源密集.

研究的目的:

调查三种大型语言模型 (LLM) 的性能,成本和快速工程权衡.
评估LLMs在加速心血管药物开发文献选过程中的潜力.
为优化生物医学研究LLM应用提供见解.

主要方法:

评估三个不同的大型语言模型.
分析与文献选相关的模型性能指标.
评估相关成本,及时制定工程策略.
对LLM在合成心血管药物开发文献方面的能力进行比较分析.

主要成果:

与传统方法相比,在文献选方面表现出显著的加速.
在评估的LLMs中确定了不同的性能和成本效率.
强调了快速工程对LLM输出的准确性和相关性的影响.
根据具体的研究需求,为选择合适的LLM提供了一个框架.

结论:

大型语言模型在加快心血管药物开发文献合成方面表现有前途.
仔细考虑模型选择,成本和快速设计对于有效实施至关重要.
法律法规可以提高识别药物发现和开发关键信息的效率.

关键词:

在这里,我们可以看到AIAIAI.在 GPT 中,GPT 必须是 GPT.在法学士 (LLM) 课程中.人工智能的人工智能是人工智能.生物标志物生物标志物生物医学生物医学生物医学信息学生物医学信息学在心脏和心脏.心脏病学心脏病学心脏病学心血管疾病的心血管疾病跨截面研究是跨截面研究.毒品毒品毒品是一种药物.药物开发是药物的发展.大型语言模型选的优化,选的优化.

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