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

Machine Learning (1465) Artificial Intelligence (1371) Clinical Information and Decision Making (1407) Clinical Informatics (1022) Development and Evaluation of Research Methods, Instruments and Tools (481) Immunology, Allergy, Rheumatology (89) Decision Support for Health Professionals (1185) New Methods (944) Electronic Health Records (1053) Tools, Programs and Algorithms (266)

Published on in Vol 9, No 11 (2021): November

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/30079, first published .
Prediction Model of Osteonecrosis of the Femoral Head After Femoral Neck Fracture: Machine Learning–Based Development and Validation Study

Prediction Model of Osteonecrosis of the Femoral Head After Femoral Neck Fracture: Machine Learning–Based Development and Validation Study

Prediction Model of Osteonecrosis of the Femoral Head After Femoral Neck Fracture: Machine Learning–Based Development and Validation Study

Authors of this article:

Huan Wang1 Author Orcid Image ;   Wei Wu2 Author Orcid Image ;   Chunxia Han1 Author Orcid Image ;   Jiaqi Zheng1 Author Orcid Image ;   Xinyu Cai3 Author Orcid Image ;   Shimin Chang4 Author Orcid Image ;   Junlong Shi5 Author Orcid Image ;   Nan Xu6 Author Orcid Image ;   Zisheng Ai1 Author Orcid Image
Article Authors Cited by (12) Tweetations (3) Metrics

Journals

  1. Xu L, Liu J, Han C, Ai Z. The Application of Machine Learning in Predicting Mortality Risk in Patients With Severe Femoral Neck Fractures: Prediction Model Development Study. JMIR Bioinformatics and Biotechnology 2022;3(1):e38226 View
  2. van de Kuit A, Oosterhoff J, Dijkstra H, Sprague S, Bzovsky S, Bhandari M, Swiontkowski M, Schemitsch E, IJpma F, Poolman R, Doornberg J, Hendrickx L. Patients With Femoral Neck Fractures Are at Risk for Conversion to Arthroplasty After Internal Fixation: A Machine‐learning Algorithm. Clinical Orthopaedics & Related Research 2022;480(12):2350 View
  3. Hu Y, Yang Q, Zhang J, Peng Y, Guang Q, Li K. Methods to predict osteonecrosis of femoral head after femoral neck fracture: a systematic review of the literature. Journal of Orthopaedic Surgery and Research 2023;18(1) View
  4. Sun S, Wang L, Lin J, Sun Y, Ma C. An effective prediction model based on XGBoost for the 12-month recurrence of AF patients after RFA. BMC Cardiovascular Disorders 2023;23(1) View
  5. Xue P, Xi H, Chen H, He S, Liu X, Du B. Predictive value of clinical features and CT radiomics in the efficacy of hip preservation surgery with fibula allograft. Journal of Orthopaedic Surgery and Research 2023;18(1) View
  6. Xu Q, Lei L, Lin Z, Zhong W, Wu X, Zheng D, Li T, Huang J, Yan T. An machine learning model to predict quality of life subtypes of disabled stroke survivors. Annals of Clinical and Translational Neurology 2024;11(2):404 View
  7. Lang F, Liu L, Wang S. Predictive modeling of perioperative blood transfusion in lumbar posterior interbody fusion using machine learning. Frontiers in Physiology 2023;14 View
  8. Dijkstra H, van de Kuit A, de Groot T, Canta O, Groot O, Oosterhoff J, Doornberg J, van den Bekerom M, Calderon S, Colaris J, Duis K, Esfahani S, DiGiovanni C, Gordon M, Guss D, IJpma F, Jaarsma R, Janssen M, Jayakumar P, Kerkhoffs G, Leighton R, van Munster B, Poolman R, Ring D, Schemtisch E, Stirler V, Tornetta P, Wijffels M. Systematic review of machine-learning models in orthopaedic trauma. Bone & Joint Open 2024;5(1):9 View
  9. Deng W, Wan J, Wang D, Geng K, Zhang G, Hou R. Experimental analysis of femoral head intraosseous vascular anastomosis in the treatment of porcine subcapital femoral neck fractures. Heliyon 2024;10(3):e25211 View
  10. Silva G, Ashford R. Using Artificial Intelligence to predict outcomes of operatively managed neck of femur fractures. British Journal of Hospital Medicine 2024;85(6):1 View
  11. Liu B, Hou G, Yang Z, Zhang Z, Zhou F, Tian Y. Machine learning models to predict osteonecrosis in patients with femoral neck fractures undergoing internal fixation. Injury 2024;55(11):111830 View
  12. Zhang L, Zhao S, Yang Z, Zheng H, Lei M. An artificial intelligence tool to assess the risk of severe mental distress among college students in terms of demographics, eating habits, lifestyles, and sport habits: an externally validated study using machine learning. BMC Psychiatry 2024;24(1) View