Summary of Reinforcement Learning in Modern Biostatistics: Constructing Optimal Adaptive Interventions, by Nina Deliu et al.
Reinforcement Learning in Modern Biostatistics: Constructing Optimal Adaptive Interventions
by Nina Deliu, Joseph Jay Williams, Bibhas Chakraborty
First submitted to arxiv on: 4 Mar 2022
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Machine Learning (cs.LG); Applications (stat.AP); Methodology (stat.ME)
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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 A unified technical survey on reinforcement learning (RL) methods is presented, with a focus on constructing adaptive interventions (AIs) in healthcare. The study bridges two AI domains – dynamic treatment regimes and just-in-time adaptive interventions in mobile health – highlighting similarities and differences between them. RL methods are applied to various types of AIs, showcasing the potential for synergies between statistical, RL, and healthcare researchers. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Reinforcement learning is a powerful tool used in health-related decision-making problems. It’s like having a personal assistant that helps you make good choices. Right now, it’s not being used as much as it could be because different groups of experts aren’t working together very well. This paper brings those groups together to show how reinforcement learning can be used to create personalized interventions for people’s health. It looks at two ways this can be done: dynamic treatment regimes and just-in-time adaptive interventions in mobile health. |
Keywords
* Artificial intelligence * Reinforcement learning
