33: TableMage: An LLM-Enhanced Python Package for Low-Code/Conversational Clinical Data Science

Music City Center 

Data analysis is essential to evidence-based medicine, yet many clinicians encounter significant technical barriers due to limited training in statistical learning and data science workflows. These challenges often result in inefficiencies, errors, and a dependency on external experts for quantitative analyses. To address this, we introduce TableMage, an open-source, user-friendly Python package tailored for clinical researchers. TableMage enhances analytical workflows through a low-code API that supports exploratory data analysis, regression modeling, and machine learning. It also features a no-code interface powered by large language models (LLMs), enabling users to conduct secure analyses of proprietary datasets via locally hosted open-source LLMs, thereby ensuring data privacy. Our benchmarks against GPT-4o Advanced Data Analysis on 21 public datasets demonstrate that TableMage delivers comparable accuracy in core data analysis tasks, superior performance in machine learning applications, and enhanced flexibility for secure data handling. By equipping clinicians with the tools to directly engage with data, TableMage fosters more efficient, accurate, and independent research.

software

data science

large language models

agents

generative AI

machine learning 

Section on Statistical Computing