arXiv:2411. 05196v3 Announce Type: replace Abstract: This study presents DhondtXAI as a SHAP-independent, D'Hondt-based attribution framework for tabular XAI.
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arXiv:2609.14902v1 Announce Type: cross Abstract: Shapley value (SV)-based methods are the prevailing framework for feature attribution in machine learning, yet existing population-level Shapley esti...
arXiv:2605. 12895v2 Announce Type: replace-cross Abstract: Clinical decision-support systems are expert systems whose recommendations clinicians act on directly, yet they are usually cleared on one aggregate accuracy number from a held-out test set.
The study evaluates a standalone large language model (LLM) versus a four‑step agentic pipeline for generating explanations of ICU mortality predictions on the eICU Demo dataset. XGBoost achieved an AUROC of 0.855 and an AUPRC of 0.332. In a 38‑case explanation subset, the standalone LLM produced one explanation with outcome leakage, while the agentic pipeline produced none; among 14 overlapping SHAP cases, the standalone LLM had higher SHAP alignment and direction consistency, whereas the agentic pipeline showed better guideline grounding, value specificity, and plausibility.
arXiv:2607. 24878v1 Announce Type: cross Abstract: Phenotype-driven diagnostic benchmarks usually report the rank of the reference disease, but they rarely reveal which plausible alternatives are ranked above it or what evidence a tool-using model examines before making its decision.
arXiv:2606. 14823v1 Announce Type: cross Abstract: Genetic evidence is enriched among approved drug targets: in an observational analysis of 26,278 target-disease pairs from Open Targets and ChEMBL, targets with any genetic association had a 3.
arXiv:2601. 05151v3 Announce Type: replace-cross Abstract: Feature selection (FS) is essential for biomarker discovery and clinical predictive modeling.