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The growing availability of open health datasets has advanced medical research and healthcare innovation. This study proposes a Large Language Model (LLM) approach that enables Exploratory Data Analysis (EDA) through natural language queries by integrating Retrieval-Augmented Generation (RAG) and post-processing mechanisms. It was evaluated using five open health datasets, encompassing both structured and unstructured data. The results show that the approach effectively describes datasets, identifies outliers, and produces diverse visualizations, including histograms and correlation heatmaps. The method demonstrates the feasibility of using LLMs to automate and democratize EDA, enhancing accessibility and interpretability in health data exploration.

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LLM-Based Solution Applied to Explore Healthcare Datasets

  • 会议论文

  • 发表日期：

    2026年

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  • Fernando Rezende Zagatti,
  • André Gomes Regino,
  • Filipe Loyola Lopes,
  • Gilson Yuuji Shimizu,
  • Rodrigo Bonacin,
  • Daniel Lucrédio,
  • Helena de Medeiros Caseli

摘要

The growing availability of open health datasets has advanced medical research and healthcare innovation. This study proposes a Large Language Model (LLM) approach that enables Exploratory Data Analysis (EDA) through natural language queries by integrating Retrieval-Augmented Generation (RAG) and post-processing mechanisms. It was evaluated using five open health datasets, encompassing both structured and unstructured data. The results show that the approach effectively describes datasets, identifies outliers, and produces diverse visualizations, including histograms and correlation heatmaps. The method demonstrates the feasibility of using LLMs to automate and democratize EDA, enhancing accessibility and interpretability in health data exploration.