Multi-classifier prediction of knee osteoarthritis progression from incomplete imbalanced longitudinal data.

ABSTRACT: Conventional inclusion criteria used in osteoarthritis clinical trials are not very effective in selecting patients who would benefit from a therapy being tested. Typically majority of selected patients show no or limited disease progression during a trial period. As a consequence, the effect of the tested treatment cannot be observed, and the efforts and resources invested in running the trial are not rewarded. This could be avoided, if selection criteria were more predictive of the future disease progression. In this article, we formulated the patient selection problem as a multi-class classification task, with classes based on clinically relevant measures of progression (over a time scale typical for clinical trials). Using data from two long-term knee osteoarthritis studies OAI and CHE

SUBMITTER: Widera P 

PROVIDER: S-EPMC7242357 | biostudies-literature | 2020 May

REPOSITORIES: biostudies-literature