LLM-Adapted Interpretation Framework for Machine Learning Models
- URL: http://arxiv.org/abs/2507.21179v1
- Date: Sat, 26 Jul 2025 15:50:08 GMT
- Title: LLM-Adapted Interpretation Framework for Machine Learning Models
- Authors: Yuqi Jin, Zihan Hu, Weiteng Zhang, Weihao Xie, Jianwei Shuai, Xian Shen, Zhen Feng,
- Abstract summary: High-performance machine learning models like XGBoost are often "black boxes," limiting their clinical adoption due to a lack of interpretability.<n>This study aims to bridge the gap between predictive accuracy and narrative transparency for sarcopenia risk assessment.
- Score: 0.3420293559931652
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Background & Aims: High-performance machine learning models like XGBoost are often "black boxes," limiting their clinical adoption due to a lack of interpretability. This study aims to bridge the gap between predictive accuracy and narrative transparency for sarcopenia risk assessment. Methods: We propose the LLM-Adapted Interpretation Framework (LAI-ML), a novel knowledge distillation architecture. LAI-ML transforms feature attributions from a trained XGBoost model into a probabilistic format using specialized techniques (HAGA and CACS). A Large Language Model (LLM), guided by a reinforcement learning loop and case-based retrieval, then generates data-faithful diagnostic narratives. Results: The LAI-ML framework achieved 83% prediction accuracy, significantly outperforming the baseline XGBoost model, 13% higher. Notably, the LLM not only replicated the teacher model's logic but also corrected its predictions in 21.7% of discordant cases, demonstrating enhanced reasoning. Conclusion: LAI-ML effectively translates opaque model predictions into trustworthy and interpretable clinical insights, offering a deployable solution to the "black-box" problem in medical AI.
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