Chunhua Weng

Abstract Electronic health records (EHRs) contain extensive multidimensional patient data, presenting challenges for the discovery of novel and meaningful clinical patterns. Unsupervised clustering of high-dimensional clinical data holds great potential for identifying novel clinical patterns. Here, we performed unsupervised clustering and characterized 100,272 patients in the Electronic Medical Records and GEnomics (eMERGE) Network.

Abstract Clinical decision making often relies on expert judgment guided by established guidelines, which can be challenging to standardize and abstract to implement. For example, selecting between gene panels and whole exome/genome sequencing (WES/WGS) for rare disease diagnosis frequently requires interpretation of evidence-based recommendations from the American College of Medical Genetics and Genomics (ACMG) guideline.

PerspectiveOnline nowOpen access Affiliations & Notes 1Department of Medicine, Brigham and Women’s Hospital, Boston, MA, USA 2Harvard Medical School, Boston, MA, USA 3Center for Biomedical Ethics and Society, Vanderbilt University Medical Center, Nashville, TN, USA 4Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN, USA 5Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, USA 6Institute for Genomic Health, Icahn School of Medicine at...