Summary of Reinforcement Learning in Dynamic Treatment Regimes Needs Critical Reexamination, by Zhiyao Luo et al.
Reinforcement Learning in Dynamic Treatment Regimes Needs Critical Reexamination
by Zhiyao Luo, Yangchen Pan, Peter Watkinson, Tingting Zhu
First submitted to arxiv on: 28 May 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 The paper critiques the application of offline reinforcement learning (RL) in dynamic treatment regimes (DTRs), citing concerns about inconsistent evaluation metrics, lack of naive and supervised learning baselines, and diverse RL formulations. It argues that a reassessment is needed to ensure reliable development of RL-based DTRs. The authors demonstrate the varying performance of RL algorithms with changes in evaluation metrics and Markov Decision Process (MDP) formulations using a publicly available Sepsis dataset. Surprisingly, some instances show RL algorithms being surpassed by random baselines subjected to policy evaluation methods and reward design. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper talks about how artificial intelligence can help make better decisions for patients with certain diseases. It looks at a type of AI called reinforcement learning (RL) that’s used in treatment plans. The authors are worried that the way RL is being used right now might not be the best, and they’re suggesting some changes to make sure it works better. They tested different ways of using RL on a big dataset and found that sometimes the AI did really well, but other times it didn’t do as well as just random choices. This makes them think we need to be more careful when making decisions with AI. |
Keywords
» Artificial intelligence » Reinforcement learning » Supervised
