A clinical decision support tool for accurate hip fracture prediction: A nationwide cohort study
Researchers have developed a machine learning tool called FRACTURE-ML that predicts hip fracture risk using nationwide health data without requiring in-person assessments. The study found the model significantly outperforms current standard screening methods in identifying at-risk individuals.
Why it matters
Automated, high-accuracy risk prediction tools can improve preventative healthcare outcomes for aging populations while reducing the burden on clinical resources.
Although hip fractures are commonly associated with functional decline, increased morbidity, and mortality, accurate models for both short- and long-term prediction that do not rely on in-person assessment remain lacking. The aim was to develop and validate a high-performing clinical decision support tool, that can be used for population screening without the need for patient assessment, for predicting hip fracture risk.
Get smarter about the news
Sign up free for a feed built around what you actually care about, Dive Deeper research on any story, and the full text of every article.
Create free accountAlready have an account? Sign in