Unsupervised machine-learning algorithms for the identification of clinical phenotypes in the osteoarthritis initiative database

• Published in: Seminars in arthritis and rheumatism Vol. 58; p. 152140
• Main Authors: Demanse, David, Saxer, Franziska, Lustenberger, Patrick, Tankó, László B., Nikolaus, Philipp, Rasin, Ilja, Brennan, Damian F., Roubenoff, Ronenn, Premji, Sumehra, Conaghan, Philip G, Schieker, Matthias
• Format: Journal Article
• Language: English
• Published: United States Elsevier Inc 01.02.2023
• Subjects: Clinical phenotypes; Cluster analysis; Disease Progression; Humans; Knee osteoarthritis; Machine Learning; Osteoarthritis, Knee - diagnostic imaging; Pain; Patient segments; Phenotype; Precision medicine; Radiography; Cluster analysis; Knee osteoarthritis; Precision medicine; Clinical phenotypes; Patient segments; Machine learning
• ISSN: 0049-0172; 1532-866X; 1532-866X
• DOI: 10.1016/j.semarthrit.2022.152140

Osteoarthritis (OA) is a complex disease comprising diverse underlying patho-mechanisms. To enable the development of effective therapies, segmentation of the heterogenous patient population is critical. This study aimed at identifying such patient clusters using two different machine learning algor...