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...
By Zizheng Zhang, Yiming Li, Justin Xu, Jinyu Wang, Rui Wang, Lei Song, Jiang Bian, David W Eyre, Jingjing Fu
arXiv:2603. 02221v2 Announce Type: replace-cross Abstract: In clinical tabular prediction, classical machine learning models with feature engineering often outperform neural methods.
By Zizheng Zhang, Yiming Li, Justin Xu, Jinyu Wang, Rui Wang, Lei Song, Jiang Bian, David W Eyre, Jingjing Fu
arXiv:2601. 22324v3 Announce Type: replace Abstract: Modern clinical practice relies on evidence-based guidelines implemented as compact scoring systems composed of a small number of interpretable decision rules.
By Silas Ruhrberg Est\'evez, Christopher Chiu, Mihaela van der Schaar
arXiv:2609.13192v1 Announce Type: new
Abstract: This study compares traditional machine learning models and Large Language Model (LLM)-generated rule-based systems for heart disease prediction using...
By Feisal Alaswad, Batoul Aljaddouh, Maher Alrahhal, Wafaa Al Nassan, Talal Bonn
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.
By Majid Lotfian Delouee, Hamed Ayoobi, Sjors G. J. G. In 't Veld, Martijn C. Schut
arXiv:2604. 09737v2 Announce Type: replace-cross Abstract: Structured prediction with large language models requires outputs that are label-accurate, ontology-constrained, structurally valid, and evidence-grounded under label imbalance and heterogeneous group difficulty.
By Samah Fodeh, Ganesh Puthiaraju, Elyas Irankhah, Afshan Khan, Sreeraj Ramachandran, Linhai Ma, Srivani Talakokkul, Sarah Schellhorn