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Multi-source AI news clustered, deduplicated, and scored 0–100 across authority, cluster strength, headline signal, and time decay.

  1. RESEARCH · Hugging Face Daily Papers English(EN) · · [2 sources]

    Explainable AI for Data-Driven Design of High-Dimensional Predictive Studies

    Researchers have developed an Exploratory AI Recommender to aid in the design of high-dimensional predictive studies, particularly in healthcare. This framework uses flexible AI to identify complex data patterns and explainable AI techniques to generate recommendations for feature exclusion, non-linear terms, and feature interactions. When applied to predict patient falls, the system suggested excluding 23 features and including 221 interactions, leading to an improved C-index from 0.805 to 0.815. AI

    IMPACT Enhances the interpretability and performance of predictive models in high-dimensional settings, potentially increasing clinical trust and adoption.

  2. RESEARCH · arXiv stat.ML English(EN) · · [2 sources]

    KAPLAN: Kolmogorov-Arnold Prognostic Learnable Activation Networks for Survival Analysis

    Researchers have developed KAPLAN-HR, a new deep learning model based on Kolmogorov-Arnold Networks (KANs) for survival analysis. This model can estimate conditional hazard rates as a joint function of covariates and time, overcoming limitations of traditional methods that require manual specification of complex effects. Evaluations on six clinical datasets show KAPLAN-HR performs comparably to or better than existing statistical and deep learning survival analysis techniques. AI

    IMPACT Introduces a novel deep learning architecture for survival analysis, potentially improving predictions in clinical and other time-to-event domains.