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Indicators of retention in remote digital health studies: a cross-study evaluation of 100, 000 participants., , , , , , , , , and 4 other author(s). npj Digit. Medicine, (2020)Geospatial divide in real-world EHR data: Analytical workflow to assess regional biases and potential impact on health equity., , , , , , and . AMIA, AMIA, (2022)DREAMTools: a Python package for scoring collaborative challenges., , , , , , , , , and 11 other author(s). F1000Research, (2015)The feasibility of using smartphones to assess and remediate depression in Hispanic/Latino individuals nationally., , , , , , and . UbiComp/ISWC Adjunct, page 854-860. ACM, (2017)Indicators of retention in remote digital health studies: A cross-study evaluation of 100, 000 participants., , , , , , , , , and 4 other author(s). CoRR, (2019)Detecting the impact of subject characteristics on machine learning-based diagnostic applications., , , , , , , , , and . npj Digit. Medicine, (2019)Understanding Participant Needs for Engagement and Attitudes towards Passive Sensing in Remote Digital Health Studies., , , , , and . PervasiveHealth, page 347-362. ACM, (2020)Long-term participant retention and engagement patterns in an app and wearable-based multinational remote digital depression study., , , , , , , , , and 19 other author(s). npj Digit. Medicine, (2023)A Permutation Approach to Assess Confounding in Machine Learning Applications for Digital Health., , , , , , and . KDD, page 54-64. ACM, (2019)Fast Similarity Search in Large-Scale Iris Databases Using High-Dimensional Hashing., , and . BDA, volume 14418 of Lecture Notes in Computer Science, page 187-200. Springer, (2023)