ArticleFrontiers of medicine2022
Hybrid deep learning model for risk prediction of fracture in patients with diabetes and osteoporosis.
Article in Frontiers of medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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14 citing papers in PubMed, 21 citations in OpenAlex.
- Intelligent Orthopedics: Machine Learning in Diagnosis of Bone Disease, Implants, and Bone Health Monitoring.Advanced healthcare materials · 2026Review
- An explainable machine learning approach to predict fragility fractures and the identification of important features.Scientific reports · 2026Article
- Artificial Intelligence in Rheumatology: From Algorithms to Clinical Impact in Osteoporosis and Chronic Inflammatory Rheumatic Diseases.Journal of clinical medicine · 2026Article
- Deep Learning-Based Assessment of the Value of Vertebral Structural Parameters in Predicting Osteoporotic Vertebral Compression Fractures on Opportunistic CT Scans.International journal of general medicine · 2026Article
- CT-based radiomics nomogram of vertebral bodies and paravertebral muscles for differential diagnosis of type 2 diabetes mellitus.Frontiers in endocrinology · 2026Article
- A CT-based radiomic model for predicting vertebral fractures in older patients with type 2 diabetes mellitus: A longitudinal study.Journal of endocrinological investigation · 2025Article
- Development and reporting of artificial intelligence in osteoporosis management.Journal of bone and mineral research : the official journal of the American Society for Bone and Mineral Research · 2024Review
- Fully Automatic Deep Learning Model for Spine Refracture in Patients with OVCF: A Multi-Center Study.Orthopaedic surgery · 2024Article
- Multi-modal molecular determinants of clinically relevant osteoporosis subtypes.Cell discovery · 2024Article
- AI algorithms for accurate prediction of osteoporotic fractures in patients with diabetes: an up-to-date review.Journal of orthopaedic surgery and research · 2023Review
- Machine learning algorithms for predicting the risk of fracture in patients with diabetes in China.Heliyon · 2023Article
- Article
- 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
- Predicting fracture risk for elderly osteoporosis patients by hybrid machine learning model.Digital healthArticle
Corrections and comments
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Authors and funding
4 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The fracture risk of patients with diabetes is higher than those of patients without diabetes due to hyperglycemia, usage of diabetes drugs, changes in insulin levels, and excretion, and this risk begins as early as adolescence. Many factors including demographic data (such as age, height, weight, and gender), medical history (such as smoking, drinking, and menopause), and examination (such as bone mineral density, blood routine, and urine routine) may be related to bone metabolism in patients with diabetes. However, most of the existing methods are qualitative assessments and do not consider the interactions of the physiological factors of humans. In addition, the fracture risk of patients with diabetes and osteoporosis has not been further studied previously. In this paper, a hybrid model combining XGBoost with deep neural network is used to predict the fracture risk of patients with diabetes and osteoporosis, and investigate the effect of patients' physiological factors on fracture risk. A total of 147 raw input features are considered in our model. The presented model is compared with several benchmarks based on various metrics to prove its effectiveness. Moreover, the top 18 influencing factors of fracture risks of patients with diabetes are determined.
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