用大模型自动生成临床试验表格和图表，省时高效。
Using Large Language Models to Generate Clinical Trial Tables and Figures
- 通过提示工程和少量样本学习，让大模型理解临床数据生成需求。
- 在ADaM格式数据上，模型能准确生成预定义的表格与图表。
- 开发了专用工具，用户提问即可自动匹配生成对应TFLs。
表格、图形和列表（TFLs）是总结临床试验数据的重要工具。在临床试验执行过程中，编制TFLs常是一项耗时的工作。本研究探索了利用大语言模型（LLMs）通过提示工程和少样本迁移学习自动化生成TFLs的可行性。基于公开的以ADaM格式呈现的临床试验数据，结果表明，只需提供提示指令，LLMs即可高效生成TFLs，展现出该方法在此领域的潜力。此外，我们开发了一个名为Clinical Trial TFL Generation Agent的保守型智能体——一个能够将用户查询匹配至预设提示，生成定制化程序以产出特定预定义TFLs的应用程序。
原文摘要 · Abstract (English)
Tables, figures, and listings (TFLs) are essential tools for summarizing clinical trial data. Creation of TFLs for reporting activities is often a time-consuming task encountered routinely during the execution of clinical trials. This study explored the use of large language models (LLMs) to automate the generation of TFLs through prompt engineering and few-shot transfer learning. Using public clinical trial data in ADaM format, our results demonstrated that LLMs can efficiently generate TFLs with prompt instructions, showcasing their potential in this domain. Furthermore, we developed a conservational agent named Clinical Trial TFL Generation Agent: An app that matches user queries to predefined prompts that produce customized programs to generate specific predefined TFLs.
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