arXiv:2603. 02221v2 Announce Type: replace-cross Abstract: In clinical tabular prediction, classical machine learning models with feature engineering often outperform neural methods.
Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.
arXiv:2603.02221v3 Announce Type: replace-cross Abstract: In clinical tabular prediction, classical machine learning models with feature engineering often outperform neural methods. LLMs are increasi...
arXiv:2606. 16337v1 Announce Type: new Abstract: Predictive modeling for clinical tabular data is central to clinical decision support and therefore requires not only strong predictive performance but also transparent decision logic.
SymboLLM-FE combines symbolic regression and large language models to automate feature engineering for tabular data. It first extracts mathematically expressive formulas that correlate strongly with the target, then refines them with LLMs to improve interpretability. Experiments on six real‑world datasets and four Kaggle competitions show that SymboLLM‑FE outperforms existing AutoFE methods while reducing the number of costly LLM calls.
arXiv:2607. 09165v1 Announce Type: cross Abstract: Achieving early and timely diagnosis and treatment for disease is a major challenge.
The paper introduces a four-step pipeline that mines decision rules in the latent space of an FT-Transformer and then translates those rules back into measurable clinical features. By treating embedding dimensions that separate patient groups as latent biomarkers, small decision trees are used to extract rules, which are then mapped to raw features using gradient-input saliency and CLS attention attribution. Across six public clinical datasets, the translated rules generally outperformed raw-feature rules, achieving significant AUROC gains, though some high-performing latent rules could not be fully captured by simple raw-feature conditions.
arXiv:2606. 31171v1 Announce Type: new Abstract: Acquiring comprehensive cross-domain biomedical profiles is often costly and time-consuming, resulting in severe data scarcity in medical research.