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

Formative Evaluation of Digital Health Interventions (2410) Artificial Intelligence (1371) Machine Learning (1465) Depression and Mood Disorders; Suicide Prevention (1363)

Published on in Vol 6, No 9 (2022): September

This is a member publication of Imperial College London (Jisc)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/36130, first published .
Predicting Depression in Patients With Knee Osteoarthritis Using Machine Learning: Model Development and Validation Study

Predicting Depression in Patients With Knee Osteoarthritis Using Machine Learning: Model Development and Validation Study

Predicting Depression in Patients With Knee Osteoarthritis Using Machine Learning: Model Development and Validation Study

Authors of this article:

Zuzanna Nowinka1 Author Orcid Image ;   M Abdulhadi Alagha1, 2 Author Orcid Image ;   Khadija Mahmoud1 Author Orcid Image ;   Gareth G Jones1 Author Orcid Image
Article Authors Cited by (4) Tweetations (5) Metrics

Journals

  1. Surendran T, Park L, Lauber M, Cha B, Jhun R, Capellini T, Kumar D, Felson D, Kolachalama V. Survival analysis on subchondral bone length for total knee replacement. Skeletal Radiology 2024;53(8):1541 View
  2. Ravi A, DeMarco E, Gebauer S, Poirier M, Hinyard L. Prevalence and Predictors of Depression in Women with Osteoarthritis: Cross-Sectional Analysis of Nationally Representative Survey Data. Healthcare 2024;12(5):502 View
  3. Tejani A, Ng Y, Xi Y, Rayan J. Understanding and Mitigating Bias in Imaging Artificial Intelligence. RadioGraphics 2024;44(5) View
  4. Nair A, Alagha M, Cobb J, Jones G. Assessing the Value of Imaging Data in Machine Learning Models to Predict Patient-Reported Outcome Measures in Knee Osteoarthritis Patients. Bioengineering 2024;11(8):824 View