The goal of this study is to understand if mobile technologies can contribute clinically meaningful metrics. By analyzing mobile technology over time and comparing to available clinical health records, supervised and unsupervised machine learning can be applied to understand potential of this technology in clinical care and outcomes.
For example, does treatment failure of proton pump inhibitors in genetic hypermetabolizers cause increased surgical procedures and how do drugs interact in patients treated with multiple... more »
The goal of this study is to ascertain clinical implications of rare genetic variants predicted to be deleterious discovered in individuals not carrying the diagnosis. Using existing phenotypes or potentially with recontact to participants physiological impact of variants on health and disease can be understood.
Characterize phenotypes of participants with rare deleterious variants of unknown significance discovered by deep sequencing through available surveys and EHR data or through recontact with additional studies.
Estimate risk for common disease or health outcomes based on known genetic factors combined with novel environmental data types such as proximity to food sources and pollution and laboratory measures of pollutant exposure, for example, what is the relative risk of genetic factors for diabetes compared to dietary habits and proximity to grocery stores?
This study would look at aspects of the home environment, including: cleaning products, address or block group for GIS mapping, proximity to roadways or certain agricultural or industrial facilities, pets, number of people living in the household, type of flooring, building structure, ventilation, type of heat, mold, and moisture level.
This study would compare those using conventional tampons to those using organic tampons or external menstrual products only and look at length of menstruation (number of days), pesticide metabolites, and pregnancy outcomes.