  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.
