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MedFeat: Model-Aware and Explainability-Driven Feature Engineering with LLMs for Clinical Tabular Prediction

arXiv – CS AI|Zizheng Zhang, Yiming Li, Justin Xu, Jinyu Wang, Rui Wang, Lei Song, Jiang Bian, David W Eyre, Jingjing Fu|March 4, 2026 at 05:00 AM|1 views

🤖AI Summary

Researchers introduce MedFeat, a new AI framework that uses Large Language Models for healthcare feature engineering in clinical tabular predictions. The system incorporates model awareness and domain knowledge to discover clinically meaningful features that outperform traditional approaches and demonstrate robustness across different hospital settings.

Key Takeaways

→MedFeat leverages LLMs with domain knowledge for automated feature engineering in healthcare prediction tasks.
→The framework incorporates model awareness and SHAP values to prioritize informative signals that downstream models struggle to learn.
→Testing shows stable improvements over baselines and generalization across different clinical environments.
→The approach discovers clinically meaningful features that remain robust under distribution shift.
→Results demonstrate potential for real-world deployment in healthcare AI systems.

#healthcare-ai #llm #feature-engineering #clinical-prediction #machine-learning #shap #medical-ai #tabular-data

Read Original →via arXiv – CS AI

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