A machine-learning tool built from Swedish national health registry data can predict hip fracture risk with high accuracy without an in-person assessment and identifies far more at-risk individuals than current clinical screening practices, according to a study published Aug. 27 in the journal PLOS Medicine by Kristian Axelsson and Mattias Lorentzon of the University of Gothenburg, Sweden, and colleagues.
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