The recent U.S. Supreme Court decision overturning Roe v. Wade and newly enacted state laws limiting or banning abortion can be expected to bring new scrutiny to the privacy vulnerabilities of electronic health records.

A national databank of de-identified voices, combined with artificial intelligence, could lead to diagnosing and treating cancer, depression, autism, Alzheimer’s disease and voice disorders.Vanderbilt University Medical Center is partnering with 11 institutions on a $14 million NIH-funded project led by the University of South Florida and Weill Cornell Medicine that aims to establish voice as a biomarker used in clinical care.

Faculty members at Vanderbilt University Medical Center will have a central role in a National Institutes of Health (NIH) program called Bridge to Artificial Intelligence, or Bridge2AI.

VCLIC FACULTY POSITIONFaculty Position Opening: The Vanderbilt Clinical Informatics Center (VCLIC), Department of Biomedical Informatics, School of Medicine, Vanderbilt University Medical Center 

Below are this week's DBMI Seminar and DBMI Research Colloquium. See below for details and Zoom info!   DBMI SEMINAR WEDNESDAY, SEPTEMBER 21, 2022 at 12:00 PM CT Event Type: DBMI Seminar Title: "Enabling Clinical Research and Innovation through a Clinical Informatics Core"

A high-throughput informatics technique developed at Vanderbilt University Medical Center that reveals associations between genetic variations and medical conditions in the electronic health record (EHR) also can identify new “post-COVID” diagnoses, according to a report in the Journal of the American Medical Informatics Association. 

One in 100 people have genetic variations that can cause potentially life-threatening heart conditions, including high cholesterol (lipid disorders), heart muscle disease (cardiomyopathies), and abnormal heart rhythms (arrhythmias). Yet the functional impact of most of these cardiovascular genetic variants — whether they disrupt normal function or are harmless — is unknown. That is about to change.

Methods relying on diagnostic codes to identify suicidal ideation and suicide attempt in Electronic Health Records (EHRs) at scale are suboptimal because suicide-related outcomes are heavily under-coded. We propose to improve the ascertainment of suicidal outcomes using natural language processing (NLP). We developed information retrieval methodologies to search over 200 million notes from the Vanderbilt EHR. Suicide query terms were extracted using word2vec. A weakly supervised approach was designed to label cases of suicidal outcomes.

The Vanderbilt University Medical Center (VUMC) Department of Biomedical Informatics's (DBMI) monthly newsletter, DBMI Digest, is now available to view. Read the August 2022 DBMI Digest here. Each DBMI Digest features department & faculty announcements, awards & appointments, educational & HR updates, funding opportunities and more. Each issue also includes a profile of one of our faculty, staff, postdocs and students.