Adaptive Recruitment Resource Allocation to Improve Cohort Representativeness in Participatory Biomedical Datasets
<|reference_start|>Adaptive Recruitment Resource Allocation to Improve Cohort Representativeness in Participatory Biomedical Datasets: Large participatory biomedical studies, studies that recruit individuals to join a dataset, are gaining popularity and investment, especially for analysis by modern AI methods. Because they purposively recruit participants, these studies are uniquely able to address a lack of historical representation, an issue that has affected many biomedical datasets. In this work, we define representativeness as the similarity to a target population distribution of a set of attributes and our goal is to mirror the U.S. population across distributions of age, gender, race, and ethnicity. Many participatory studies recruit at several institutions, so we introduce a computational approach to adaptively allocate recruitment resources among sites to improve representativeness. In simulated recruitment of 10,000-participant cohorts from medical centers in the STAR Clinical Research Network, we show that our approach yields a more representative cohort than existing baselines. Thus, we highlight the value of computational modeling in guiding recruitment efforts.<|reference_end|>
@article{borza2024adaptive,
    title={Adaptive Recruitment Resource Allocation to Improve Cohort
  Representativeness in Participatory Biomedical Datasets},
    author={Victor Borza, Andrew Estornell, Ellen Wright Clayton, Chien-Ju Ho,
  Russell Rothman, Yevgeniy Vorobeychik, Bradley Malin},
    journal={arXiv preprint arXiv:2408.01375},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.01375},
    primaryClass={cs.LG cs.CY}
}