Summary of Synrl: Aligning Synthetic Clinical Trial Data with Human-preferred Clinical Endpoints Using Reinforcement Learning, by Trisha Das et al.
SynRL: Aligning Synthetic Clinical Trial Data with Human-preferred Clinical Endpoints Using Reinforcement Learning
by Trisha Das, Zifeng Wang, Afrah Shafquat, Mandis Beigi, Jason Mezey, Jacob Aptekar, Jimeng Sun
First submitted to arxiv on: 11 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 In this paper, the authors propose a novel method for generating synthetic patient data in clinical trials using reinforcement learning. The existing approaches disregard the usage requirements of these data and only use post-hoc assessments. SynRL customizes the generated data to meet the user-specified requirements for synthetic data outcomes and endpoints. It includes a data value critic function to evaluate the quality of the generated data and uses reinforcement learning to align the data generator with the users’ needs based on the critic’s feedback. The authors demonstrate the advantages of SynRL in improving the quality of the generated synthetic data while keeping privacy risks low, using four clinical trial datasets. This method can be utilized as a general framework for customizing data generation of multiple types of synthetic data generators. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Synthetic patient data is used to help doctors and researchers share medical records without sharing real patient information. Researchers have been trying to make better synthetic data, but most methods don’t consider what the users need from this data. The authors propose a new way to generate synthetic data using reinforcement learning, which helps create better data that meets user requirements. They tested their method on four clinical trial datasets and showed it works well. This method can be used for different types of synthetic data generators. |
Keywords
» Artificial intelligence » Reinforcement learning » Synthetic data
