This paper is in the following e-collection/theme issue:

Clinical Information and Decision Making (1426) Artificial Intelligence (1409) Electronic Health Records (1066) Machine Learning (1491) Natural Language Processing (704) Nails and Musculoskeletal (24)

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

Authors of this article:

Yasmeen Adar Almog1 Author Orcid Image ;   Angshu Rai1 Author Orcid Image ;   Patrick Zhang1 Author Orcid Image ;   Amanda Moulaison1 Author Orcid Image ;   Ross Powell1 Author Orcid Image ;   Anirban Mishra1 Author Orcid Image ;   Kerry Weinberg1 Author Orcid Image ;   Celeste Hamilton2 Author Orcid Image ;   Mary Oates3 Author Orcid Image ;   Eugene McCloskey4 Author Orcid Image ;   Steven R Cummings5 Author Orcid Image
Article Authors Cited by (29) Tweetations (11) Metrics

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
  20. Hong N, Whittier D, Glüer C, Leslie W. The potential role for artificial intelligence in fracture risk prediction. The Lancet Diabetes & Endocrinology 2024;12(8):596 View
  21. Lehmann O, Mineeva O, Veshchezerova D, Häuselmann H, Guyer L, Reichenbach S, Lehmann T, Demler O, Everts-Graber J, Wenger M, Oser S, Toniolo M, Schmid G, Studer U, Ziswiler H, Steiner C, Krappel F, Pancaldi P, Kashiwagi M, Frey D, Zäch R, Weber H. Fracture risk prediction in postmenopausal women with traditional and machine learning models in a nationwide, prospective cohort study in Switzerland with validation in the UK Biobank. Journal of Bone and Mineral Research 2024 View
  22. Jain R, Polley E, Weiner M, Iwamaye A, Huang E, Vokes T. An electronic health record (EHR)-based risk calculator can predict fractures comparably to FRAX: a proof-of-concept study. Osteoporosis International 2024;35(12):2117 View
  23. Dickens L, Jain R. An Update on the Fracture Risk Assessment Tool. Endocrinology and Metabolism Clinics of North America 2024;53(4):531 View
  24. Li Y, Jin D, Zhang Y, Li W, Jiang C, Ni M, Liao N, Yuan H. Utilizing artificial intelligence to determine bone mineral density using spectral CT. Bone 2025;192:117321 View
  25. Kailasanathan N, Somayaji S, Baza M, Srivastava G, Ulaganathan S, Yenduri G, Ravindranath V, Alsabaan M. Remote Assistance for Bone-Fractured Patients using Deep Learning Models. Applied Artificial Intelligence 2024;38(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