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Wednesday, May 10, 2023 – 12:00PM to 1:00PM
Md Mobashir Hasan Shandhi, PhD; American Heart Association Postdoctoral Fellow, Department of Biomedical Engineering, Duke University, with host Andrew Olson, MPP; Associate Director, Policy Strategy and Solutions for Health Data Science, Duke AI Health
Longitudinal digital health studies combine information from digital devices, such as commercial wearable devices, and patient-reported data, such as surveys, from participants. While the ubiquitous adoption of smartphones and access to the internet supports the development of large-scale and distributed digital health studies, there are challenges in collecting representative data as a result of low adherence to, engagement with, and regularity of performing study tasks such as filling out surveys and charging and wearing devices. These challenges may result in a study population that is not representative of the general population or the population group of interest. Artificial Intelligence tools developed based on a non-representative population have a higher chance to fail to generalize in the real-world deployment of such technologies and may not work for underrepresented and underserved communities. In this seminar, the speaker will share his research group's experience in conducting longitudinal digital health studies for COVID-19 monitoring, the challenges the researchers faced to collect data from a representative population, and how his team developed a guideline to mitigate demographic imbalance in bring-your-own-device (BYOD) design-based digital health studies. Furthermore, the speaker will also share how his team developed a machine learning method based intelligent allocation method for COVID-19 diagnostic testing in a resource-limited setting (when we have limited diagnostic tests, like the earlier phase of the pandemic and onset of new variants) using wearable and survey data collected during the longitudinal CovIdentify study.
Please join us for this lunchtime virtual seminar. The presentation will be accessible to a broad audience, including those with no prior background in health data science or artificial intelligence.