ArticleScientific reports2025
A machine learning tool for predicting newly diagnosed osteoporosis in primary healthcare in the Stockholm Region.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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3 citing papers in PubMed.
- Elevated pro-BNP and low-grade inflammation are associated with low bone mineral density in systemic sclerosis, a case control study.Arthritis research & therapy · 2026Article
- From Molecular Pathways to Artificial Intelligence: Advancing the Understanding and Management of Osteosarcopenia.International journal of molecular sciences · 2026Review
- Article
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8 authors.
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Abstract
Improving accuracy and timeliness for osteoporosis diagnosis could help prevent fragility fractures, morbidity, and mortality for older individuals. Osteoporosis is an often silent health condition, especially as regards vertebral fractures, and WHO issued a call to action for primary care to lead efforts in screening, assessing, and managing diseases such as osteoporosis. We used a machine learning method, Stochastic Gradient Boosting (SGB), to identify what diagnoses in a primary care setting predict a new osteoporosis diagnosis, using a sex- and age-matched case-control design. Cases of new osteoporosis (ICD-10 code: M80, M81, M82) were identified across all outpatient care settings during 2012-2019. We included individuals aged ≥ 40 years old, stratified by sex and age-groups 40-65 years and > 65 years old. Controls were sampled from outpatients that did not have osteoporosis at any time during 2010-2019. Using the SGB model, we ranked the most important diagnoses related to newly diagnosed osteoporosis, presented as the normalized relative influence (NRI) score with a corresponding odds ratio of marginal effects (OR
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