![]() ![]() NLP reduced the missingness of vital signs by 31%. We then compared C3PO to Convenience Samples including all individuals from the same EHR with complete data, but without a longitudinal primary care requirement. We assessed the validity of C3PO by deploying established risk models for myocardial infarction/stroke and atrial fibrillation. We utilized natural language processing (NLP) to recover vital signs from unstructured notes. Using the Mass General Brigham multi-institutional EHR, we approximated a community-based cohort by sampling patients receiving longitudinal primary care between 2001-2018 (Community Care Cohort Project, n = 520,868). health record (EHR) datasets are statistically powerful but are subject to ascertainment bias and missingness. 18 Demoulas Center for Cardiac Arrhythmias, Massachusetts General Hospital, Boston, MA, USA. 17 Cardiovascular Disease Initiative, Broad Institute of Harvard and the Massachusetts Institute of Technology, Cambridge, MA, USA.
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