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[2602.19761] Ensemble Machine Learning and Statistical Procedures for Dynamic Predictions of Time-to-Event Outcomes
Summary
This article discusses the use of ensemble machine learning techniques, specifically the Super Learner framework, to improve dynamic predictions for time-to-event outcomes in precision medicine, focusing on primary biliary cholangitis patients.
Why It Matters
Dynamic predictions are crucial in precision medicine, particularly for managing chronic conditions. This study enhances predictive accuracy by integrating various statistical and machine learning methods, potentially improving patient outcomes and clinical decision-making.
Key Takeaways
- Ensemble methods like Super Learner can enhance predictive accuracy in medical outcomes.
- Combining different models can yield better results than any single model alone.
- The study focuses on primary biliary cholangitis, showcasing practical applications in healthcare.
Statistics > Machine Learning arXiv:2602.19761 (stat) [Submitted on 23 Feb 2026] Title:Ensemble Machine Learning and Statistical Procedures for Dynamic Predictions of Time-to-Event Outcomes Authors:Nina van Gerwen, Sten Willemsen, Bettina E. Hansen, Christophe Corpechot, Marco Carbone, Cynthia Levy, Maria-Carlota Londõno, Atsushi Tanaka, Palak Trivedi, Alejandra Villamil, Gideon Hirschfield, Dimitris Rizopoulos View a PDF of the paper titled Ensemble Machine Learning and Statistical Procedures for Dynamic Predictions of Time-to-Event Outcomes, by Nina van Gerwen and 11 other authors View PDF HTML (experimental) Abstract:Dynamic predictions for longitudinal and time-to-event outcomes have become a versatile tool in precision medicine. Our work is motivated by the application of dynamic predictions in the decision-making process for primary biliary cholangitis patients. For these patients, serial biomarker measurements (e.g., bilirubin and alkaline phosphatase levels) are routinely collected to inform treating physicians of the risk of liver failure and guide clinical decision-making. Two popular statistical approaches to derive dynamic predictions are joint modelling and landmarking. However, recently, machine learning techniques have also been proposed. Each approach has its merits, and no single method exists to outperform all others. Consequently, obtaining the best possible survival estimates is challenging. Therefore, we extend the Super Learner framework to combine dy...