Wei-Qi Wei Archives
Drug allergy labels in medical records power searches for gene-drug associations
Jun. 17, 2021— by Paul Govern A meta-analysis published in 1998 in the Journal of the American Medical Association estimated that, leaving aside overdoses and cases of drug abuse, 2.2 million patients admitted to U.S. hospitals in 1994 (6.7% of the total) were either admitted due to a serious adverse drug reaction or had a serious ADR...
Team analyzed clinical notes to identify COVID symptoms
Apr. 15, 2021—At the start of the COVID-19 pandemic, public health authorities first had to identify and draw attention to common signs and symptoms of the disease.
Grant supports speedy sorting of health records by phenotype
Feb. 11, 2021—Wei-Qi Wei, MD, PhD, assistant professor of Biomedical Informatics and scientific director of the Precision Phenotyping Core at the Center for Precision Medicine, has been awarded a four-year, $1.7 million grant from the National Institutes of Health (grant GM139891) to continue
COVID-associated delays for elective services studied
Dec. 17, 2020—This spring in the U.S., there were widespread delays in elective health care procedures and screenings. Hospitals, in observance of federal guidelines, were, for a time, conserving beds and protective equipment in preparation for a surge in COVID-19 admissions. And, perhaps on a more prolonged basis, patients in many areas of the country stayed away due to anxiety over catching COVID-19 from other patients or their health care team.
New tool rapidly identifies health records for studies
Oct. 29, 2020—Electronic health records (EHR) are increasingly a resource for biomedical discovery, and automated searches for records that reflect a phenotype of interest, typically a disease, are a common starting point.
VUMC-led network to focus on polygenic risk for common diseases
Jul. 1, 2020—With the aid of a $75 million, five-year grant renewal, the Electronic Medical Records and Genomics Network (eMERGE) will venture beyond its current focus on monogenic disease to scoring research participants’ relative risk for complex heritable diseases such as cardiovascular disease, chronic kidney disease and type 2 diabetes.
AI maps routes to heart disease
Oct. 7, 2019—Machine learning on unlabeled electronic health record data has shed light on the emergence of cardiovascular disease.
Data diving for health
Sep. 23, 2015—To most effectively use electronic health records for research, investigators should query multiple components of the record to identify patients with specific diseases.
