Electronic health records (EHRs) contain rich temporal data about infectious diseases, but an optimal approach to identify infections remains undefined. Using the Research Program, we developed computable phenotypes for respiratory viruses by integrating billing codes, prescriptions, and laboratory results within 90-day episodes. Phenotypes computed from 265,222 participants yielded cohorts ranging from 238 (adenovirus) to 28,729 (SARS-CoV-2) cases.
View Article and Find Full Text PDFSummary: With the rapid growth of genetic data linked to electronic health record (EHR) data in huge cohorts, large-scale phenome-wide association study (PheWAS) have become powerful discovery tools in biomedical research. PheWAS is an analysis method to study phenotype associations utilizing longitudinal EHR data. Previous PheWAS packages were developed mostly with smaller datasets and with earlier PheWAS approaches.
View Article and Find Full Text PDFSummary: With the rapid growth of genetic data linked to electronic health record data in huge cohorts, large-scale phenome-wide association study (PheWAS), have become powerful discovery tools in biomedical research. PheWAS is an analysis method to study phenotype associations utilizing longitudinal electronic health record (EHR) data. Previous PheWAS packages were developed mostly in the days of smaller biobanks and with earlier PheWAS approaches.
View Article and Find Full Text PDFAntibiotics are a known cause of idiosyncratic drug-induced liver injury (DILI). According to the Centers for Disease Control and Prevention, the five most commonly prescribed antibiotics in the United States are azithromycin, ciprofloxacin, cephalexin, amoxicillin, and amoxicillin-clavulanate. We quantified the frequency of acute DILI for these common antibiotics in the All of Us Research Program, one of the largest electronic health record (EHR)-linked research cohorts in the United States.
View Article and Find Full Text PDFType 2 diabetes (T2D) is a heterogeneous disease that develops through diverse pathophysiological processes. To characterise the genetic contribution to these processes across ancestry groups, we aggregate genome-wide association study (GWAS) data from 2,535,601 individuals (39.7% non-European ancestry), including 428,452 T2D cases.
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