MedFeat: Model-Aware and Explainability-Driven Feature Engineering with LLMs for Clinical Tabular Prediction
arXiv:2603.02221v2 Announce Type: replace Abstract: In clinical tabular prediction, classical machine learning models with feature engineering often outperform neural methods. LLMs are increasingly used to automate this process, acting as domain experts that propose diverse feature transformations to boost downstream performance. However, existing LLM-based methods decouple feature generation from the downstream model: the LLM receives no signal about which features currently drive predictions or where the model's representational capacity falls short, so proposals are neither targeted to prom
The rapid advancement of LLMs has created new opportunities for automating complex tasks, including feature engineering, which was previously a manual and expert-driven process in clinical AI.
This research could significantly improve the accuracy and explainability of clinical AI models, leading to better diagnostic and prognostic tools and fostering greater trust in AI-driven healthcare decisions.
Feature engineering, a critical bottleneck in deploying robust classical machine learning models in clinical settings, can now be made more efficient, targeted, and explainable through LLM integration.
- · Healthcare AI developers
- · Patients
- · Clinical researchers
- · LLM developers
- · Manual feature engineering specialists
- · Traditional clinical ML methods with limited explainability
Clinical AI models will achieve higher performance and better interpretability, accelerating their adoption in medical practice.
The demand for specialized medical data scientists focusing solely on manual feature engineering might decrease as LLM-driven tools become more prevalent.
Increased trust in AI-powered diagnostics could lead to a re-evaluation of medical training curricula, incorporating more AI-literacy and LLM-driven tools.
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Read at arXiv cs.LG