The study presents a curated dataset of 616 fresh embryo-transfer cycles and develops a Hybrid Attention Network (HAN) combined with logistic regression to predict live birth after IVF/ICSI. The HAN+LR ensemble achieved the best performance with an AUC‑ROC of 0.710, outperforming both individual components and other machine‑learning baselines. The differentially weighted trophectoderm morphology emerged as the most important predictor across models.
By Abdul Raheem Shahzad, Jinxin Chen, Ming Zhang, Juan Deng
arXiv:2602. 03957v2 Announce Type: replace Abstract: Background: Under-five mortality in Bangladesh remains uneven despite national progress.
By Md Muhtasim Munif Fahim, M. Monimul Huq, M. Sabiruzzaman, Md Rezaul Karim
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.
By Di Zhu, Chen Xie, Haoyun Zhang, Zihan Wei, Ziwei Wang, Jiazhao Shi, Ziyu Wang, Qiyang Xie
The study audited ten different classifiers—including linear, tree‑ensemble, neural, glass‑box, and tabular foundation models—on national health survey data to predict myocardial infarction. By systematically removing features that could cause target leakage, the authors found that all models’ AUROC scores collapsed into a narrow band, indicating that reported high accuracy in prior work was largely due to leakage rather than model sophistication. The glass‑box explainable boosting machine performed comparably to other models while being much faster, and the authors demonstrated that fairness, calibration, and uncertainty can be audited and repaired without sacrificing performance.
By Raad Bin Tareaf, Murad Al-Rajab, Samia Loucif, Samer Ellaham, Cedric Schmitz
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.
By Rohith Reddy Bellibatlu, Manpreet Singh, Yash Jajoo, Shyamal Lakhanpal, Abhishek Israni
The paper introduces a framework that distinguishes two causes of saturation in clinical prediction: a learner gap, where the model fails to use available information, and a measurement‑channel ceiling, where the recorded variables limit performance. It provides theoretical characterizations, finite‑sample diagnostics, and empirical audits across three large cohorts, showing that well‑tuned models approach the frontier while deficient learners leave large gaps. A PRISMA‑guided synthesis across 104 tasks reveals consistent channel‑level patterns, suggesting that improving the learner or the measurement channel can audit and potentially lift performance.
By Sayeed Shafayet Chowdhury, Nusrat Jahan, Snehasis Mukhopadhyay, Shiaofen Fang, Vijay R. Ramakrishnan