Saved in:
| Main Authors: | , , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2409.12046 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866909319666073600 |
|---|---|
| author | Yang, Yumeng Krusche, Peter Pantoja, Kristyn Shi, Cheng Ludmir, Ethan Roberts, Kirk Zhu, Gen |
| author_facet | Yang, Yumeng Krusche, Peter Pantoja, Kristyn Shi, Cheng Ludmir, Ethan Roberts, Kirk Zhu, Gen |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_12046 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Using Large Language Models to Generate Clinical Trial Tables and Figures Yang, Yumeng Krusche, Peter Pantoja, Kristyn Shi, Cheng Ludmir, Ethan Roberts, Kirk Zhu, Gen Computation and Language 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. |
| title | Using Large Language Models to Generate Clinical Trial Tables and Figures |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2409.12046 |