Observational studyPloS one2020
Osteoporotic hip fracture prediction from risk factors available in administrative claims data - A machine learning approach.
Observational study in PloS one, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers, 3 of them syntheses that pooled it.
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The trial behind it
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Who cites it
24 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Artificial Intelligence Approaches for Osteoporotic Fracture Risk Prediction Using Administrative Health Data: A Systematic Review.Calcified tissue international · 2026Pooled it
- Predictive value of machine learning on fracture risk in osteoporosis: a systematic review and meta-analysis.BMJ open · 2023Pooled it
- Machine learning algorithms for diagnosis of hip bone osteoporosis: a systematic review and meta-analysis study.Biomedical engineering online · 2023Pooled it
- Comment on "Trends in the incidence of secondary hip fractures and osteoporosis treatment in Japan (FY2012-FY2023)".Journal of bone and mineral metabolism · 2026Article
- An explainable machine learning approach to predict fragility fractures and the identification of important features.Scientific reports · 2026Article
- Knowledge mapping of osteoporotic fractures: a bibliometric analysis from 2014 to 2024.Archives of osteoporosis · 2025Review
- Applications of machine learning and deep learning in musculoskeletal medicine: a narrative review.European journal of medical research · 2025Review
- Biomechanical perspectives on image-based hip fracture risk assessment: advances and challenges.Frontiers in endocrinology · 2025Review
- Prognostic Factors of Hip Fracture in Elderly: A Systematic Review.International journal of preventive medicine · 2024Review
- Exploring the impact of pathogenic microbiome in orthopedic diseases: machine learning and deep learning approaches.Frontiers in cellular and infection microbiology · 2024Review
- AI algorithms for accurate prediction of osteoporotic fractures in patients with diabetes: an up-to-date review.Journal of orthopaedic surgery and research · 2023Review
- Development and validation of a machine learning-based fall-related injury risk prediction model using nationwide claims database in Korean community-dwelling older population.BMC geriatrics · 2023Article
- Advantages and Disadvantages of Random Forest Models for Prediction of Hip Fracture Risk Versus Mortality Risk in the Oldest Old.JBMR plus · 2023Article
- Development and validation of common data model-based fracture prediction model using machine learning algorithm.Osteoporosis international : a journal established as result of cooperation between the European Foundation for Osteoporosis and the National Osteoporosis Foundation of the USA · 2023Article
- Effect of Artificial Intelligence or Machine Learning on Prediction of Hip Fracture Risk: Systematic Review.Journal of bone metabolism · 2023Article
- Development and validation of sex-specific hip fracture prediction models using electronic health records: a retrospective, population-based cohort study.EClinicalMedicine · 2023Article
- Interpretable Deep-Learning Approaches for Osteoporosis Risk Screening and Individualized Feature Analysis Using Large Population-Based Data: Model Development and Performance Evaluation.Journal of medical Internet research · 2023Article
- Operational Definition Identifying Osteoporotic Vertebral Fractures in the Claims Database.Journal of Korean medical science · 2022Article
- Development of a Spine X-Ray-Based Fracture Prediction Model Using a Deep Learning Algorithm.Endocrinology and metabolism (Seoul, Korea) · 2022Article
- Prediction Models for Osteoporotic Fractures Risk: A Systematic Review and Critical Appraisal.Aging and disease · 2022Review
Corrections and comments
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Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveHip fractures are among the most frequently occurring fragility fractures in older adults, associated with a loss of quality of life, high mortality, and high use of healthcare resources. The aim was to apply the superlearner method to predict osteoporotic hip fractures using administrative claims data and to compare its performance to established methods.
methodsWe devided claims data of 288,086 individuals aged 65 years and older without care level into a training (80%) and a validation set (20%). Subsequently, we trained a superlearner algorithm that considered both regression and machine learning algorithms (e.g., support vector machines, RUSBoost) on a large set of clinical risk factors. Mean squared error and measures of discrimination and calibration were employed to assess prediction performance.
resultsAll algorithms used in the analysis showed similar performance with an AUC ranging from 0.66 to 0.72 in the training and 0.65 to 0.70 in the validation set. Superlearner showed good discrimination in the training set but poorer discrimination and calibration in the validation set.
conclusionsThe superlearner achieved similar predictive performance compared to the individual algorithms included. Nevertheless, in the presence of non-linearity and complex interactions, this method might be a flexible alternative to be considered for risk prediction in large datasets.
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