Work

Machine Learning Adds to Clinical and CAC Assessments in Predicting 10-Year CHD and CVD Deaths

Year: 2020

Type: article

Study design: Observational Study

Source: JACC. Cardiovascular imaging

DOI: 10.1016/j.jcmg.2020.08.024

Authors: Rine Nakanishi, Piotr J. Slomka, Richard Rios, Julian Betancur, Michael J. Blaha, Khurram Nasir, Michael D. Miedema, John A. Rumberger, Heidi Gransar, Leslee J. Shaw, Alan Rozanski, Matthew J. Budoff, Daniel S. Berman

Institutions: Toho University, The Lundquist Institute, Harbor–UCLA Medical Center, Cedars-Sinai Medical Center, University of California, Los Angeles, Johns Hopkins University, Johns Hopkins Medicine, Johns Hopkins Hospital, Minneapolis Heart Institute Foundation, Princeton University, Weill Cornell Medicine, Icahn School of Medicine at Mount Sinai

Language: English

Topic: Cardiac Imaging and Diagnostics

Subfield: Radiology, Nuclear Medicine and Imaging

Field: Medicine

Domain: Health Sciences

SDG: Good health and well-being

Open Access status: bronze

Funders: Dr. Miriam and Sheldon G. Adelson Medical Research Foundation, National Institutes of Health, National Heart, Lung, and Blood Institute

Awards: G1370936357, Quantitative Prediction of Disease and Outcomes from Next Generation SPECT and CT, G682208693

OpenAlex ID: W3097577799

FWCI: 7.42

Cites: 30

Cited by: 103

Related to: 10