arXiv:2605. 29058v2 Announce Type: replace Abstract: Bayesian Networks (BNs) are of interest from an explainable AI viewpoint, offering transparent probabilistic models for decision support.
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.
arXiv:2607. 15394v1 Announce Type: new Abstract: Black-box models limit the adoption of artificial intelligence in medicine due to their lack of interpretability and reproducibility.
The paper introduces a statistically grounded framework for interpretable, rule-based clinical classification using Bernoulli Naïve Bayes (BNB). It employs supervised chi‑square‑guided binarization to convert continuous medical variables into binary indicators, enabling BNB to handle continuous data while maintaining transparency. On three benchmark datasets—Pima Indians Diabetes, Wisconsin Breast Cancer, and Heart Failure Prediction—the method achieved AUCs of 0.800, 0.984, and 0.919, respectively, and demonstrated reliable probability calibration through cross‑validated analysis and post‑hoc beta calibration.
arXiv:2606. 07141v1 Announce Type: cross Abstract: Language models trained for clinical disease inference are trained on patient data, which may include sensitive and private information, and data owners may request the removal of their data from a trained model due to privacy or copyright concerns.
The paper introduces ACTMED, a diagnostic framework that combines Bayesian Experimental Design with large language models to emulate real‑world clinical reasoning. ACTMED actively selects the most informative test at each step, using LLMs to simulate patient states and update beliefs without needing task‑specific training data. The authors evaluate the system on real datasets, demonstrating improvements in diagnostic accuracy, interpretability, and efficient resource use while keeping clinicians involved in the decision loop.
arXiv:2509.19375v2 Announce Type: replace-cross Abstract: Large language models are increasingly used for clinical text classification, where overconfident misclassifications can directly affect pati...
Methodological progress in artificial intelligence (AI) for electronic health records (EHRs) depends on our ability to determine which algorithms work better, and under which conditions. However, such...
The study re‑implements 12 AI algorithms for electronic health records within a unified framework and evaluates them on MIMIC‑IV and NWICU datasets. It compares expert‑authored clinically meaningful tasks with randomly generated tasks, finding that pairwise algorithm comparisons transfer well across task families and datasets, yet clinically meaningful tasks show stronger task‑method interactions. The results also reveal that newer algorithms do not consistently outperform older ones, with gradient‑boosted trees remaining highly competitive when combined with modern EHR representations.
This paper introduces a hierarchical Bayesian multitask learning model that assumes a shared sparsity structure across different binary classification tasks. The authors develop a variational inference algorithm for efficient posterior approximation and evaluate the method on synthetic data and pooled microbiome studies. Results show superior support recovery in synthetic experiments and robust, well‑calibrated predictions with informative taxa selection in microbiome classification.
arXiv:2608. 15725v1 Announce Type: new Abstract: Predictive models in clinical and regulated settings must be accurate and fully auditable.
arXiv:2509. 07605v2 Announce Type: replace-cross Abstract: Class imbalance poses a significant challenge to supervised classification, particularly in critical domains like medical diagnostics and anomaly detection where minority class instances are rare.
arXiv:2608. 04046v1 Announce Type: cross Abstract: Survival analysis is an established framework for analyzing time-to-event data, yet many clinical machine learning studies still binarize the outcome before model training.