University of Oulu

Widera, P., Welsing, P.M.J., Ladel, C. et al. Multi-classifier prediction of knee osteoarthritis progression from incomplete imbalanced longitudinal data. Sci Rep 10, 8427 (2020).

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

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Author: Widera, Paweł1; Welsing, Paco M. J.2; Ladel, Christoph3;
Organizations: 1School of Computing Science, Newcastle University, 1 Science Square, Newcastle, NE4 5TG, UK
2Department of Rheumatology & Clinical Immunology, University Medical Center Utrecht, Heidelberglaan 100, 3584 CX, Utrecht, Netherlands
3Merck, Frankfurter Str. 250, 64293, Darmstadt, Germany
4Biosciences Institute, Newcastle University, International Centre for Life, Newcastle, NE1 3BZ, UK
5Immuno-inflammation Center of Therapeutic Innovation, Institut de Recherches Internationales Servier, Suresnes, France
6Novel Human Genetics Research Unit, GlaxoSmithKline, Collegeville, PA, 19426, USA
7Department of Orthopedics, University Medical Center Utrecht, Heidelberglaan 100, 3584 CX, Utrecht, Netherlands
8Department of Biomechanical Engineering, Delft University of Technology, Mekelweg 2, 2628 CD, Delft, Netherlands
9Department of Regenerative Medicine, State Research Institute Centre for Innovative Medicine, Santariskiu 5, 08661, Vilnius, Lithuania
10Research Unit of Medical Imaging, Physics and Technology, University of Oulu, Aapistie 5A, FIN-90230, Oulu, Finland
11Centre for Sport, Exercise and Osteoarthritis Research Versus Arthritis, Queen’s Medical Centre, Nottingham, NG7 2UH, UK
Format: article
Version: published version
Access: open
Online Access: PDF Full Text (PDF, 2.8 MB)
Persistent link:
Language: English
Published: Springer Nature, 2020
Publish Date: 2020-09-08


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.

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Series: Scientific reports
ISSN: 2045-2322
ISSN-E: 2045-2322
ISSN-L: 2045-2322
Volume: 10
Issue: 1
Article number: 8427
DOI: 10.1038/s41598-020-64643-8
Type of Publication: A1 Journal article – refereed
Field of Science: 217 Medical engineering
3126 Surgery, anesthesiology, intensive care, radiology
Funding: The research leading to these results has received support from the Innovative Medicines Initiative Joint Undertaking under Grant Agreement no. 115770, resources of which are composed of financial contribution from the European Union’s Seventh Framework Programme (FP7/2007–2013) and EFPIA companies’ in kind contribution. See and
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