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

MedFeat is a novel feature engineering framework designed for clinical tabular prediction, integrating model-awareness and feature importance signals to enhance feature discovery in LLMs. The framework demonstrates a statistically significant average improvement of over 10% compared to state-of-the-art baselines across various clinical tasks, addressing challenges such as class imbalance and interpretability in healthcare data. This advancement is crucial for practitioners as it allows for more targeted and effective feature transformations, potentially leading to improved model performance in clinical applications.