ArticleScientific reports2021
Bone mineral density response prediction following osteoporosis treatment using machine learning to aid personalized therapy.
Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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Who cites it
16 citing papers in PubMed.
- Trial
- Artificial Intelligence-Driven Precision Bisphosphonate Therapy in Osteoporosis: Mechanisms, Clinical Applications, Risks, and Future Directions.Orthopaedic surgery · 2026Review
- Artificial Intelligence in Rheumatology: From Algorithms to Clinical Impact in Osteoporosis and Chronic Inflammatory Rheumatic Diseases.Journal of clinical medicine · 2026Article
- Establishing correlation between bone mineral apposition rate and physiological loading using machine learning regressor.Scientific reports · 2025Article
- Impact of Frailty and Other Factors as Estimated by HU to Predict Response to Anabolic Bone Medications.Journal of clinical medicine · 2025Article
- Association of novel inflammatory markers with osteoporosis index in older spine osteoporosis patients: NHANES 1999-2018 cross-sectional study.Scientific reports · 2025Article
- Deciphering the gut microbiome's metabolic code: pathways to bone health and novel therapeutic avenues.Frontiers in endocrinology · 2025Review
- Application of artificial intelligence in osteoporosis: a review.Frontiers in medicine · 2025Review
- Genetic Insights Into the Relationship Between Menstrual Factors and Site-/Age-Specific Bone Mineral Density.International journal of genomics · 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
- Machine learning application for prediction of surgical site infection after posterior cervical surgery.International wound journal · 2024Article
- Machine learning prediction model for treatment responders in patients with primary biliary cholangitis.JGH open : an open access journal of gastroenterology and hepatology · 2023Article
- New Horizons: Artificial Intelligence Tools for Managing Osteoporosis.The Journal of clinical endocrinology and metabolism · 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
- Development and internal validation of a machine-learning-developed model for predicting 1-year mortality after fragility hip fracture.BMC geriatrics · 2022Article
- Osteoporosis in 10 years time: a glimpse into the future of osteoporosis.Therapeutic advances in musculoskeletal disease · 2022Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Osteoporosis is a global health problem for ageing populations. The goals of osteoporosis treatment are to improve bone mineral density (BMD) and prevent fractures. One major obstacle that remains a great challenge to achieve the goals is how to select the best treatment regimen for individual patients. We developed a computational model from 8981 clinical variables, including demographic data, diagnoses, laboratory results, medications, and initial BMD results, taken from 10-year period of electronic medical records to predict BMD response after treatment. We trained 7 machine learning models with 13,562 osteoporosis treatment instances [comprising 5080 (37.46%) inadequate treatment responses and 8482 (62.54%) adequate responses] and selected the best model (Random Forests with area under the receiver operating curve of 0.70, accuracy of 0.69, precision of 0.70, and recall of 0.89) to individually predict treatment responses of 11 therapeutic regimens, then selected the best predicted regimen to compare with the actual regimen. The results showed that the average treatment response of the recommended regimens was 9.54% higher than the actual regimens. In summary, our novel approach using a machine learning-based decision support system is capable of predicting BMD response after osteoporosis treatment and personalising the most appropriate treatment regimen for an individual patient.
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