ArticleNature communications2025
Comprehensive interaction modeling with machine learning improves prediction of disease risk in the UK Biobank.
Article in Nature communications, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Explainable Plasma Proteomics-Based Machine Learning for Osteoporosis Diagnosis, Prognosis, and Protein Biomarker Discovery in the UK Biobank.FASEB journal : official publication of the Federation of American Societies for Experimental Biology · 2026Article
- Classification of Alcohol Use Disorder With Explainable AI.Alcohol, clinical & experimental research · 2026Article
- How Can Clinicians Decide if AI-Enabled Risk Prediction Models Are Fit for Purpose for Real World Use?Journal of evaluation in clinical practice · 2026Article
- Identifying predictor factors for asthma using machine learning: evidence from the English Longitudinal Study of Ageing.BMC public health · 2026Article
- Ultra-Processed Foods and the Cardiovascular-Kidney-Metabolic Continuum: Integrating Epidemiological, Multi-Omics, and Translational Evidence.Nutrients · 2026Review
- AI-based multiomics profiling reveals complementary omics contributions to personalized prediction of cardiovascular disease.Nature communications · 2026Article
- Letter to the Editor of Annals of Surgical Oncology Concerning "Risk Factors for Gastric Cancer in Patients with Lynch Syndrome: A Systematic Review and Meta-analysis".Annals of surgical oncology · 2026Article
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Authors and funding
2 authors.
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Abstract
Understanding how risk factors interact to jointly influence disease risk can provide insights into disease development and improve risk prediction. Here we introduce survivalFM, a machine learning extension to the widely used Cox proportional hazards model that enables scalable estimation of all potential pairwise interaction effects on time-to-event outcomes. The method approximates interaction effects using a low-rank factorization, allowing it to overcome the computational and statistical limitations typically associated with high-dimensional interaction modeling. Applied to the UK Biobank dataset across nine disease examples and diverse clinical and omics risk factors, survivalFM improves prediction performance in terms of discrimination, explained variation, and reclassification in 30.6%, 41.7%, and 94.4% of the scenarios tested, respectively. In a clinical cardiovascular risk prediction scenario using the established QRISK3 model, the method adds predictive value by identifying interactions beyond the age interaction effects currently included. These results demonstrate that comprehensive modeling of interactions can facilitate advanced insights into disease development and improve risk predictions.
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