  Osteoarthritis (OA) is the most common musculoskeletal disease, which has no
cure. Knee OA (KOA) is one of the highest causes of disability worldwide, and
it costs billions of United States dollars to the global community. Prediction
of KOA progression has been of high interest to the community for years, as it
can advance treatment development through more efficient clinical trials and
improve patient outcomes through more efficient healthcare utilization.
Existing approaches for predicting KOA, however, are predominantly static, i.e.
consider data from a single time point to predict progression many years into
the future, and knee level, i.e. consider progression in a single joint only.
Due to these and related reasons, these methods fail to deliver the level of
predictive performance, which is sufficient to result in cost savings and
better patient outcomes. Collecting extensive data from all patients on a
regular basis could address the issue, but it is limited by the high cost at a
population level. In this work, we propose to go beyond static prediction
models in OA, and bring a novel Active Sensing (AS) approach, designed to
dynamically follow up patients with the objective of maximizing the number of
informative data acquisitions, while minimizing their total cost over a period
of time. Our approach is based on Reinforcement Learning (RL), and it leverages
a novel reward function designed specifically for AS of disease progression in
more than one part of a human body. Our method is end-to-end, relies on
multi-modal Deep Learning, and requires no human input at inference time.
Throughout an exhaustive experimental evaluation, we show that using RL can
provide a higher monetary benefit when compared to state-of-the-art baselines.
