Published on in Vol 22, No 10 (2020): October

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/22550, first published .
Deep Learning With Electronic Health Records for Short-Term Fracture Risk Identification: Crystal Bone Algorithm Development and Validation

Deep Learning With Electronic Health Records for Short-Term Fracture Risk Identification: Crystal Bone Algorithm Development and Validation

Deep Learning With Electronic Health Records for Short-Term Fracture Risk Identification: Crystal Bone Algorithm Development and Validation

Journals

  1. Lv H, Yang X, Wang B, Wang S, Du X, Tan Q, Hao Z, Liu Y, Yan J, Xia Y. Machine Learning–Driven Models to Predict Prognostic Outcomes in Patients Hospitalized With Heart Failure Using Electronic Health Records: Retrospective Study. Journal of Medical Internet Research 2021;23(4):e24996 View
  2. Manco L, Maffei N, Strolin S, Vichi S, Bottazzi L, Strigari L. Basic of machine learning and deep learning in imaging for medical physicists. Physica Medica 2021;83:194 View
  3. Smets J, Shevroja E, Hügle T, Leslie W, Hans D. Machine Learning Solutions for Osteoporosis—A Review. Journal of Bone and Mineral Research 2020;36(5):833 View
  4. McCloskey E, Borgstrom F, Cooper C, Harvey N, Javaid M, Lorentzon M, Kanis J. Short time horizons for fracture prediction tools: time for a rethink. Osteoporosis International 2021;32(6):1019 View
  5. Li Z, Zhang X, Ding L, Du K, Yan J, Chan M, Wu W, Li S. Deep learning approach for guiding three‐dimensional computed tomography reconstruction of lower limbs for robotically‐assisted total knee arthroplasty. The International Journal of Medical Robotics and Computer Assisted Surgery 2021;17(5) View
  6. Curtis E, Reginster J, Al-Daghri N, Biver E, Brandi M, Cavalier E, Hadji P, Halbout P, Harvey N, Hiligsmann M, Javaid M, Kanis J, Kaufman J, Lamy O, Matijevic R, Perez A, Radermecker R, Rosa M, Thomas T, Thomasius F, Vlaskovska M, Rizzoli R, Cooper C. Management of patients at very high risk of osteoporotic fractures through sequential treatments. Aging Clinical and Experimental Research 2022;34(4):695 View
  7. Möller S, Skjødt M, Yan L, Abrahamsen B, Lix L, McCloskey E, Johansson H, Harvey N, Kanis J, Rubin K, Leslie W. Prediction of imminent fracture risk in Canadian women and men aged 45 years or older: external validation of the Fracture Risk Evaluation Model (FREM). Osteoporosis International 2022;33(1):57 View
  8. Suh B, Yu H, Kim H, Lee S, Kong S, Kim J, Choi J. 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 2023;25:e40179 View
  9. Mutasa S, Yi P. Clinical Artificial Intelligence Applications. Radiologic Clinics of North America 2021;59(6):1013 View
  10. Khalid S, Pineda-Moncusí M, El-Hussein L, Delmestri A, Ernst M, Smith C, Libanati C, Toth E, Javaid M, Cooper C, Abrahamsen B, Prieto-Alhambra D. Predicting Imminent Fractures in Patients With a Recent Fracture or Starting Oral Bisphosphonate Therapy: Development and International Validation of Prognostic Models. Journal of Bone and Mineral Research 2021;36(11):2162 View
  11. Kong S, Shin C. Applications of Machine Learning in Bone and Mineral Research. Endocrinology and Metabolism 2021;36(5):928 View
  12. Pedoia V, Caliva F, Kazakia G, Burghardt A, Majumdar S. Augmenting Osteoporosis Imaging with Machine Learning. Current Osteoporosis Reports 2021;19(6):699 View
  13. Zhang A, Khatri S, Balmaceno-Criss M, Alsoof D, Daniels A. Medical optimization of osteoporosis for adult spinal deformity surgery: a state-of-the-art evidence-based review of current pharmacotherapy. Spine Deformity 2023;11(3):579 View
  14. Steiger E, Kroll L. Patient Embeddings From Diagnosis Codes for Health Care Prediction Tasks: Pat2Vec Machine Learning Framework. JMIR AI 2023;2:e40755 View
  15. Li Z, Zhao W, Lin X, Li F. AI algorithms for accurate prediction of osteoporotic fractures in patients with diabetes: an up-to-date review. Journal of Orthopaedic Surgery and Research 2023;18(1) View
  16. Wu Y, Chao J, Bao M, Zhang N. Predictive value of machine learning on fracture risk in osteoporosis: a systematic review and meta-analysis. BMJ Open 2023;13(12):e071430 View
  17. Larrainzar-Garijo R, Fernández-Tormos E, Collado-Escudero C, Alcantud Ibáñez M, Oñorbe-San Francisco F, Marin-Corral J, Casadevall D, Donaire-Gonzalez D, Martínez-Sanchez L, Cabal-Hierro L, Benavent D, Brañas F. Predictive model for a second hip fracture occurrence using natural language processing and machine learning on electronic health records. Scientific Reports 2024;14(1) View
  18. Kekatpure A, Kekatpure A, Deshpande S, Srivastava S. Development of a diagnostic support system for distal humerus fracture using artificial intelligence. International Orthopaedics 2024;48(5):1303 View
  19. Sheng J, Xu D, Hu P, Li L, Huang T. Mining Multimorbidity Trajectories and Co-Medication Effects from Patient Data to Predict Post–Hip Fracture Outcomes. ACM Transactions on Management Information Systems 2024;15(2):1 View

Books/Policy Documents

  1. Hong N, Park Y, You S, Rhee Y. Artificial Intelligence in Medicine. View
  2. Hong N, Park Y, You S, Rhee Y. Artificial Intelligence in Medicine. View
  3. Yang H, Li Y. Artificial Intelligence, Machine Learning, and Deep Learning in Precision Medicine in Liver Diseases. View