Summary of Adaptive Recruitment Resource Allocation to Improve Cohort Representativeness in Participatory Biomedical Datasets, by Victor Borza et al.
Adaptive Recruitment Resource Allocation to Improve Cohort Representativeness in Participatory Biomedical Datasets
by Victor Borza, Andrew Estornell, Ellen Wright Clayton, Chien-Ju Ho, Russell Rothman, Yevgeniy Vorobeychik, Bradley Malin
First submitted to arxiv on: 2 Aug 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computers and Society (cs.CY)
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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 novel study tackles the issue of representation bias in biomedical datasets by introducing a computational approach to optimize participant recruitment for large-scale studies. The research focuses on mirroring the U.S. population demographics across age, gender, race, and ethnicity distributions. To achieve this goal, the team proposes an adaptive allocation method for allocating recruitment resources among multiple sites. Simulations of 10,000-participant cohorts from medical centers in the STAR Clinical Research Network demonstrate the effectiveness of this approach, outperforming existing baselines. The study highlights the potential benefits of computational modeling in guiding recruitment efforts and improving representativeness in biomedical datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large biomedical studies that let people join are becoming more popular. These studies can fix a big problem with many medical datasets: they often don’t have enough people from different backgrounds. In this study, researchers want to make sure the people who join these studies are representative of the U.S. population. They propose a new way to decide where to recruit participants to get a better mix of ages, genders, races, and ethnicities. By testing their approach with simulated data from real medical centers, they show that it works better than other methods. This study shows how using computers can help make sure biomedical studies are more fair and representative. |
