Stanford University Develop Predictive Analytic Tool for Cardiovascular Disease

Sep 12, 2018 | EHR, Electronic Health Records, Machine Learning, Predictive Analytics

Kate Monica reports in EHRIntelligence
September 10, 2018 – A team of researchers from Stanford University created a personal health management tool that combines EHR data with machine learning to accurately diagnose patients with abdominal aortic aneurysm, also called AAA. A new report recently published in Cell describes how researchers integrated genome and EHR data into a new machine learning framework to predict patient diagnoses of the heart condition.  See the report: https://www.cell.com/cell/fulltext/S0092-8674(18)30916-4. The cardiovascular disease is asymptomatic as it grows. As a result, healthcare providers often diagnose the condition at a late stage. By analyzing genome and EHR data using machine learning, researchers attempted to diagnose the condition earlier for more timely treatment. Researchers also performed whole genome sequencing on patients with the form of aneurysm and modeled personal genomes with EHR data to assess the effectiveness of adjusting personal lifestyles to manage patient health. The study serves as a proof-of-principle for using available clinical information to diagnose the heart condition earlier and promote healthy lifestyle changes for improved health outcomes.

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