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The article introduces a decision‑analytic variable selection algorithm that optimizes clinical prediction models by balancing predictive performance against the costs and harms of measuring variables. It exhaustively compares net benefit across all predictor combinations, with a backward‑elimination extension for high‑dimensional data, and selects variables only when their added predictive value outweighs their measurement costs. The method, implemented in the R package NBvarsel, was demonstrated on simulated data and three real case studies, producing parsimonious models that maintained discrimination and calibration while reducing unnecessary clinical burden.
arXiv:2609.23999v1 Announce Type: new Abstract: How large language models (LLMs) integrate patient risk with clinical cost tradeoffs remains poorly understood. We investigated how four open-weight LL...
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:2608. 08920v1 Announce Type: new Abstract: Perioperative risk prediction models are often limited by narrow surgical populations, incomplete intraoperative data, poor calibration, and limited interpretability.
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.