Fracture Risk Estimation in Post-menopausal Women: A Comparison of Machine Learning Methods and Software Systems

Fracture risk prediction in post-menopausal women relies on established scoring tools, but whether machine learning methods could outperform or complement traditional software-based tools in a real-world commercial insurance population had not been evaluated.

Target RWE researchers compared ML algorithms against traditional fracture risk estimation tools in a cohort of commercially insured post-menopausal women, evaluating predictive performance and characterizing which patient characteristics drive differential model performance.

Demonstrating that ML methods can improve fracture risk stratification supports the development of more accurate patient selection tools for osteoporosis prevention programs.

Machine learning fracture risk prediction evaluation for precision medicine in osteoporosis drug programs, ML vs. traditional tool performance comparison for real-world patient risk stratification