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PIE-Med: Predicting, Interpreting and Explaining Medical Recommendations

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Advances in Information Retrieval (ECIR 2025)

Abstract

We introduce PIE-Med, a novel Clinical Decision Support System (CDSS) that integrates Graph Convolutional Networks (GCNs) and Large Language Models (LLMs) to deliver interpretable medical recommendations. PIE-Med leverages GCNs to generate recommendations based on patients’ health data and validated medical knowledge, ensuring clinical relevance and robustness. Interpretability algorithms evaluate the model’s reasoning, while LLM agents translate these insights into natural language explanations for clear, context-aware recommendations. By using LLMs as auxiliary reasoning agents rather than primary decision-makers, PIE-Med mitigates risks like hallucination and biased reasoning common in LLM-driven systems. Our code is publicly available on GitHub: https://github.com/picuslab/PIE-Med.

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Notes

  1. 1.

    Pandas: https://pandas.pydata.org.

  2. 2.

    Our fork of PyHealth for GNN models: https://github.com/LaErre9/PyHealth.

  3. 3.

    We used GPT-3.5 for these experiments.

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Acknowledgments

This work was conducted with the financial support of (1) the PNRR MUR project PE0000013-FAIR and (2) the Italian ministry of economic development, via the ICARUS (Intelligent Contract Automation for Rethinking User Services) project (CUP: B69J23000270005).

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Correspondence to Antonio Romano .

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Romano, A., Riccio, G., Postiglione, M., Moscato, V. (2025). PIE-Med: Predicting, Interpreting and Explaining Medical Recommendations. In: Hauff, C., et al. Advances in Information Retrieval. ECIR 2025. Lecture Notes in Computer Science, vol 15576. Springer, Cham. https://doi.org/10.1007/978-3-031-88720-8_2

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  • DOI: https://doi.org/10.1007/978-3-031-88720-8_2

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