CORE-Cox learns low-rank Cox coefficients across outcomes in a source cohort then applies regularized adaptation to a target cohort, yielding C-index gains from 0.733 to 0.766 in UK Biobank and 0.628 to 0.658 in MIMIC-IV Asian subgroups under nested cross-validation.
Tripod+ ai statement: updated guidance for reporting clinical prediction models that use regression or machine learning methods
6 Pith papers cite this work. Polarity classification is still indexing.
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An AI recommender system improves Cox Proportional Hazards model performance for predicting patient falls by suggesting 23 feature exclusions, 2 non-linear terms, and 221 interactions, raising C-index from 0.805 to 0.815.
CARIS is a new agentic LLM framework that automates clinical research workflows from planning to reporting in a coding-free and privacy-preserving manner, achieving high completeness scores on heterogeneous datasets.
Only 12.2% of 3,967 eligible prediction model studies share code, with shared repositories frequently lacking dependency specifications and modular structure needed for reproducibility.
The paper introduces ClinQueryAgent, a conversational agent that converts natural language queries into database queries for population health management while keeping patient data secure, and reports its use by 128 staff across 15 NHS practices covering 148,319 patients.
citing papers explorer
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Structured Transfer Learning for Survival Risk Stratification in Data-Sparse Clinical Cohorts
CORE-Cox learns low-rank Cox coefficients across outcomes in a source cohort then applies regularized adaptation to a target cohort, yielding C-index gains from 0.733 to 0.766 in UK Biobank and 0.628 to 0.658 in MIMIC-IV Asian subgroups under nested cross-validation.
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Explainable AI for Data-Driven Design of High-Dimensional Predictive Studies
An AI recommender system improves Cox Proportional Hazards model performance for predicting patient falls by suggesting 23 feature exclusions, 2 non-linear terms, and 221 interactions, raising C-index from 0.805 to 0.815.
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Coding-Free and Privacy-Preserving Agentic Framework for Data-Driven Clinical Research
CARIS is a new agentic LLM framework that automates clinical research workflows from planning to reporting in a coding-free and privacy-preserving manner, achieving high completeness scores on heterogeneous datasets.
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Code Sharing In Prediction Model Research: A Scoping Review
Only 12.2% of 3,967 eligible prediction model studies share code, with shared repositories frequently lacking dependency specifications and modular structure needed for reproducibility.
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ClinQueryAgent: A Conversational Agent for Population Health Management
The paper introduces ClinQueryAgent, a conversational agent that converts natural language queries into database queries for population health management while keeping patient data secure, and reports its use by 128 staff across 15 NHS practices covering 148,319 patients.
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