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

Lookup NU author(s): Dr Pawel Widera, Professor John Loughlin, Dr Jaume Bacardit

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This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).


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 CHECK, we tested multiple algorithms and learning process configurations (including multi-classifier approaches, cost-sensitive learning, and feature selection), to identify the best performing machine learning models. We examined the behaviour of the best models, with respect to prediction errors and the impact of used features, to confirm their clinical relevance. We found that the model-based selection outperforms the conventional inclusion criteria, reducing by 20-25% the number of patients who show no progression. This result might lead to more efficient clinical trials.


Publication metadata

Author(s): Widera P, Welsing PM, Ladel C, Loughlin J, Lafeber FP, Petit-Dop F, Larkin J, Weinans H, Mobasheri A, Bacardit J

Publication type: Article

Publication status: Published

Journal: Scientific Reports

Year: 2020

Volume: 10

Print publication date: 21/05/2020

Online publication date: 21/05/2020

Acceptance date: 23/04/2020

Date deposited: 22/05/2020

ISSN (electronic): 2045-2322

Publisher: Nature

URL: https://doi.org/10.1038/s41598-020-64643-8

DOI: 10.1038/s41598-020-64643-8

PubMed id: 32439879


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