用大模型自动优化临床表格数据特征，提升预测效果并增强可解释性。
MedFeat: Model-Aware and Explainability-Driven Feature Engineering with LLMs for Clinical Tabular Prediction
- 基于下游模型反馈动态生成特征，避免盲目尝试
- 在多种临床任务中平均性能提升超10%（显著优于基线）
- 适合需要高可解释性的医疗预测场景
在临床表格预测中，传统机器学习结合特征工程常优于神经网络方法。大语言模型（LLMs）被用于自动化这一过程，充当领域专家提出多样化的特征变换以提升下游性能。然而，现有方法将特征生成与下游模型解耦：大模型无法获取当前哪些特征驱动预测或模型表示能力的短板，导致建议缺乏针对性且不契合学习器的归纳偏置。这一缺陷在医疗数据中尤为突出，因其存在类别不平衡、异构特征空间和严格的可解释性要求。本文提出MedFeat，首个受机器学习实践流程启发的特征工程框架，通过模型感知与特征重要性信号迭代引导特征发现，适用于临床表格学习。我们在多个具有挑战性的现实临床任务上评估了MedFeat，结果表明其统计显著优于当前最优基线，在不同归纳偏置模型下平均性能提升超过10%。
原文摘要 · Abstract (English)
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 promising regions of the feature space nor tailored to the learner's inductive bias. This shortcoming is amplified in healthcare data, which simultaneously exhibits class imbalance, heterogeneous feature spaces, and strict interpretability requirements. In this paper, we propose MedFeat, the first feature engineering framework inspired by the workflow of machine learning practitioners, leveraging model-awareness and feature importance signals to iteratively guide feature discovery for clinical tabular learning. We evaluate MedFeat on a broad range of challenging real-world clinical tasks and show that it statistically significantly outperforms state-of-the-art baselines, with an average improvement of more than 10% over the baseline across models with distinct inductive biases.
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