Using Large Language Models to Generate Clinical Trial Tables and Figures Article Swipe

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Yumeng Yang , Peter Krusche , Kristyn Pantoja , Shi Cheng , Ethan B. Ludmir , Kirk Roberts , Guixiang Zhu ·

YOU? · · 2024 · Open Access · · DOI: https://doi.org/10.48550/arxiv.2409.12046

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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Type: preprint
Language: en
Landing Page: http://arxiv.org/abs/2409.12046
PDF: https://arxiv.org/pdf/2409.12046
OA Status: green
Related Works: 10
OpenAlex ID: https://openalex.org/W4403746987

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DOI: https://doi.org/10.48550/arxiv.2409.12046

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Title: Using Large Language Models to Generate Clinical Trial Tables and Figures

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Type: preprint

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Language: en

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Publication year: 2024

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Publication date: 2024-09-18

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Authors: Yumeng Yang, Peter Krusche, Kristyn Pantoja, Shi Cheng, Ethan B. Ludmir, Kirk Roberts, Guixiang Zhu

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Landing page: https://arxiv.org/abs/2409.12046

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PDF URL: https://arxiv.org/pdf/2409.12046

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OA status: green

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OA URL: https://arxiv.org/pdf/2409.12046

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Concepts: Computer science, Natural language processing

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Cited by: 0

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Related works (count): 10

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