ArticleJBMR plus2020
A Novel Fracture Prediction Model Using Machine Learning in a Community-Based Cohort.
Article in JBMR plus, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 37 papers, 3 of them syntheses that pooled it.
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Who cites it
37 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Artificial intelligence in osteoporosis screening and fracture risk prediction among aging populations: a bibliometric evidence map.Frontiers in endocrinology · 2026Pooled it
- [Application and prospect of machine learning in orthopaedic trauma].Zhongguo xiu fu chong jian wai ke za zhi = Zhongguo xiufu chongjian waike zazhi = Chinese journal of reparative and reconstructive surgery · 2023Pooled it
- Machine learning algorithms for diagnosis of hip bone osteoporosis: a systematic review and meta-analysis study.Biomedical engineering online · 2023Pooled it
- Editorial: Prediction of Survival in Hepatocellular Cancer-Rise and Fall of the Machines. Authors' Reply.Alimentary pharmacology & therapeutics · 2026Article
- An explainable machine learning approach to predict fragility fractures and the identification of important features.Scientific reports · 2026Article
- Machine learning prediction for menopause women with low bone mass: a multicenter and retrospective study.Scientific reports · 2026Article
- Development of Simple Assessment tool for predict imminent risk of Fracture in Elderly women.The Korean journal of internal medicine · 2026Article
- Knowledge mapping of osteoporotic fractures: a bibliometric analysis from 2014 to 2024.Archives of osteoporosis · 2025Review
- Emerging applications of feature selection in osteoporosis research: from biomarker discovery to clinical decision support.Journal of bone and mineral research : the official journal of the American Society for Bone and Mineral Research · 2025Review
- Synthetic Data-Enhanced Classification of Prevalent Osteoporotic Fractures Using Dual-Energy X-Ray Absorptiometry-Based Geometric and Material Parameters.Endocrinology and metabolism (Seoul, Korea) · 2025Article
- PrOsteoporosis: predicting osteoporosis risk using NHANES data and machine learning approach.BMC research notes · 2025Article
- A predictive nomogram for selective screening of asymptomatic vertebral fractures: The Vietnam Osteoporosis Study.Osteoporosis and sarcopenia · 2025Article
- The application progress of artificial intelligence in osteoporosis diagnosis.Frontiers in artificial intelligence · 2025Review
- An explainable web application based on machine learning for predicting fragility fracture in people living with HIV: data from Beijing Ditan Hospital, China.Frontiers in cellular and infection microbiology · 2025Article
- Predicting Superaverage Length of Stay in COPD Patients with Hypercapnic Respiratory Failure Using Machine Learning.Journal of inflammation research · 2025Article
- Discriminatory Accuracy of Fracture Risk Assessment Tool in Asian Populations: A Systematic Review and Meta-Analysis.Journal of bone metabolism · 2024Article
- Application of Machine Learning Algorithms to Predict Osteoporotic Fractures in Women.Korean journal of family medicine · 2024Article
- Clinical Applicability of Machine Learning in Family Medicine.Korean journal of family medicine · 2024Article
- Artificial intelligence-driven radiomics: developing valuable radiomics signatures with the use of artificial intelligence.BJR artificial intelligence · 2024Article
- Applications of Artificial Intelligence Methods for the Prediction of Osteoporotic Fractures.Life (Basel, Switzerland) · 2023Article
Corrections and comments
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Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The prediction of fracture risk in osteoporotic patients has been a topic of interest for decades, and models have been developed for the accurate prediction of fracture, including the fracture risk assessment tool (FRAX). As machine-learning methodologies have recently emerged as a potential model for medical prediction tools, we aimed to develop a novel fracture prediction model using machine-learning methods in a prospective community-based cohort. In this study, 2227 participants (1257 females) with a baseline bone mineral density (BMD) and trabecular bone score were enrolled from the Ansung cohort. The primary endpoint was the fragility fractures reported by patients or confirmed by X-rays. We used 3 different models: CatBoost, support vector machine (SVM), and logistic regression. During a mean 7.5-year follow-up (range, 2.5 to 10 years), fragility fractures occurred in 537 (25.6%) of participants. In predicting total fragility fractures, the area under the curve (AUC) values of the CatBoost, SVM, and logistic regression models were 0.688, 0.500, and 0.614, respectively. The AUC value of CatBoost was significantly better than that of FRAX (0.663;
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