Summary of Active Sensing Of Knee Osteoarthritis Progression with Reinforcement Learning, by Khanh Nguyen et al.
Active Sensing of Knee Osteoarthritis Progression with Reinforcement Learning
by Khanh Nguyen, Huy Hoang Nguyen, Egor Panfilov, Aleksei Tiulpin
First submitted to arxiv on: 5 Aug 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This research proposes a novel approach to predicting knee osteoarthritis (KOA) progression using Active Sensing (AS), a method designed to dynamically collect data while minimizing costs. The existing static approaches, which consider single time points and joint levels, fail to deliver sufficient predictive performance. To overcome this limitation, the authors employ Reinforcement Learning (RL) and multi-modal Deep Learning to develop an end-to-end model that requires no human input at inference time. The proposed method leverages a novel reward function tailored for AS of disease progression in multiple body parts. Experimental results show that RL-based AS outperforms state-of-the-art baselines, providing a higher monetary benefit. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Knee osteoarthritis is a common condition with no cure. Researchers want to predict how it will progress so they can develop better treatments and improve patient outcomes. Right now, the best methods are based on single snapshots of data, which isn’t very accurate. This study proposes a new approach that collects data in real-time while keeping costs low. It uses machine learning techniques and rewards itself for making good predictions. The results show that this method is more effective than existing approaches. |
Keywords
» Artificial intelligence » Deep learning » Inference » Machine learning » Multi modal » Reinforcement learning
