arXiv:2607. 09165v1 Announce Type: cross Abstract: Achieving early and timely diagnosis and treatment for disease is a major challenge.
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arXiv:2603. 02221v2 Announce Type: replace-cross Abstract: In clinical tabular prediction, classical machine learning models with feature engineering often outperform neural methods.
arXiv:2603.02221v3 Announce Type: replace-cross Abstract: In clinical tabular prediction, classical machine learning models with feature engineering often outperform neural methods. LLMs are increasi...
arXiv:2608.29301v1 Announce Type: new Abstract: Predicting future organ dysfunction in Intensive Care Unit (ICU) patients is critical for early clinical intervention, yet existing machine learning ap...
arXiv:2602. 19502v2 Announce Type: replace Abstract: Agentic AI systems are increasingly capable of autonomous data science workflows, yet clinical prediction tasks demand domain expertise that purely automated approaches struggle to provide.
arXiv:2607. 17508v1 Announce Type: cross Abstract: We introduce Retrieval-Augmented Interpretable Learning (RAIL), a probabilistic meta-learning framework for zero-shot generation of task-specific interpretable models that synthesizes coefficient-space structure from natural-language task descriptions and a memory of previously learned task-specific predictors.
The paper introduces the General Demographic Pre-trained (GDP) model, a lightweight foundation model that learns representations from the two most common clinical attributes—age and sex. By optimizing encoding and visit‑reordering strategies, GDP embeddings are shown to improve predictive performance when concatenated with raw features across various disease and geographic cohorts. The model outperforms several state‑of‑the‑art tabular foundation models and tree‑based algorithms, demonstrating that enriched demographic embeddings can enhance classification tasks while remaining fully compatible with standard classifiers.