Adaptive Recruitment Resource Allocation to Improve Cohort Representativeness in Participatory Biomedical Datasets
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arXiv
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| Main Authors: | , , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866909278292410368 |
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| author | Borza, Victor Estornell, Andrew Clayton, Ellen Wright Ho, Chien-Ju Rothman, Russell Vorobeychik, Yevgeniy Malin, Bradley |
| author_facet | Borza, Victor Estornell, Andrew Clayton, Ellen Wright Ho, Chien-Ju Rothman, Russell Vorobeychik, Yevgeniy Malin, Bradley |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2408_01375 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Adaptive Recruitment Resource Allocation to Improve Cohort Representativeness in Participatory Biomedical Datasets Borza, Victor Estornell, Andrew Clayton, Ellen Wright Ho, Chien-Ju Rothman, Russell Vorobeychik, Yevgeniy Malin, Bradley Machine Learning Computers and Society 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. |
| title | Adaptive Recruitment Resource Allocation to Improve Cohort Representativeness in Participatory Biomedical Datasets |
| topic | Machine Learning Computers and Society |
| url | https://arxiv.org/abs/2408.01375 |