Fei Wang, Ph.D., assistant professor of healthcare policy & research at Weill Cornell Medicine, has received a grant from NSF Smart and Connected Health Program titled EAGER: Patient Similarity Learning with Massive Clinical Data and Its Applications in Cohort Identification.
A critical step to make sure that opportunities exist for conducting large-scale precision medicine research, researchers must ensure that cohorts are identified by defining including and exclusion criteria that algorithmically select sets of patients. Most existing research has criteria for generating those patient cohorts and defining them manually. Of course, this method makes the entire process slow, labor intensive, and unscalable.
Dr. Wang's project will develop patient similarity learning algorithms from various kinds of patient related data such as EHR, pharmaceutical R&D and genomic information, to enable automatic cohort identification, which will accelerate the research of precision medicine.
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