Machine-learning for osteoarthritis research

Kluzek, S.; Mattei, T.A.

Abstract

Machine learning (ML) algorithms have the ability to automatically learn and improve from experience without being specifically directed. There has been great optimism that such techniques may improve scientific biomedical research in several fields1. Although conventional statistical modeling remains the method of choice for etiology-driven and explanatory analyses, ML consists in an interesting approach to identify new associations and patterns in large datasets. This is particularly important now, as it has become possible to generate large quantities of data from each study participant, from sources such as high-resolution MRI imaging, serum sample analysis, genome sequencing, and electronic medical records.

Citation

Kluzek, S., & Mattei, T. (2019). Machine-learning for osteoarthritis research. Osteoarthritis and Cartilage, 27(7), 977-978. https://doi.org/10.1016/j.joca.2019.04.005

• Journal Article Type: Article
• Acceptance Date: Apr 17, 2019
• Online Publication Date: Apr 17, 2019
• Publication Date: 2019-07
• Deposit Date: May 20, 2020
• Journal: Osteoarthritis and Cartilage
• Print ISSN: 1063-4584
• Publisher: Elsevier
• Peer Reviewed: Peer Reviewed
• Volume: 27
• Issue: 7
• Pages: 977-978
• DOI: https://doi.org/10.1016/j.joca.2019.04.005
• Public URL: https://nottingham-repository.worktribe.com/output/4471117
• Publisher URL: https://www.oarsijournal.com/article/S1063-4584(19)30927-6/fulltext
• Related Public URLs: https://www.sciencedirect.com/science/article/abs/pii/S1063458419309276