All Of Us Research Program
PulseAugur coverage of All Of Us Research Program — every cluster mentioning All Of Us Research Program across labs, papers, and developer communities, ranked by signal.
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AI-assisted study finds psychosocial factors more linked to cognitive difficulty than environment
A new research paper published on arXiv explores the relationship between environmental factors and subjective cognitive difficulties. Using data from the All of Us Research Program, the study found that short-term envi…
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New toolkit audits intersectional fairness in clinical AI models
A new research paper introduces FairLogue, a toolkit designed to audit intersectional fairness in clinical machine learning models. The study applied FairLogue to two existing models using the All of Us dataset, evaluat…
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New ensemble deep clustering method improves EHR patient stratification
Researchers have developed an ensemble-based deep clustering approach to improve patient stratification using electronic health records (EHRs). This new method aggregates cluster assignments from multiple embedding dime…
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New method estimates selection bias impact on medical AI models
Researchers have developed a new method to estimate the potential impact of selection bias on machine learning models, particularly in healthcare settings. This approach provides a practical upper bound on worst-case mo…
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Reinforcement learning optimizes physical activity for health biomarkers
Researchers have developed a novel offline reinforcement learning algorithm to create personalized physical activity recommendations. This algorithm analyzes step count data and health biomarkers from the All of Us Rese…
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AI model predicts chronic rhinosinusitis using nationwide EHR data
Researchers have developed a new method to predict chronic rhinosinusitis (CRS) using nationwide electronic health record (EHR) data. The approach leverages two years of pre-diagnostic history and a hybrid feature-selec…
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New method tackles missing covariate data in large-scale population studies
Researchers have developed a new augmented transfer regression learning method to address situations where crucial covariates are entirely missing in a target population, a common issue with large datasets like the UK B…