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Full-Text Articles in Medicine and Health Sciences
A Community-Wide Collaboration To Reduce Cardiovascular Disease Risk: The Hearts Of Sonoma County Initiative., Allen Cheadle, Michelle Rosaschi, Dolores Burden, Monica Ferguson, Bo Greaves, Lori Houston, Jennifer Mcclendon, Jerome Minkoff, Maggie Jones, Pam Schwartz, Jean Nudelman, Mary Maddux-Gonzalez
A Community-Wide Collaboration To Reduce Cardiovascular Disease Risk: The Hearts Of Sonoma County Initiative., Allen Cheadle, Michelle Rosaschi, Dolores Burden, Monica Ferguson, Bo Greaves, Lori Houston, Jennifer Mcclendon, Jerome Minkoff, Maggie Jones, Pam Schwartz, Jean Nudelman, Mary Maddux-Gonzalez
Articles, Abstracts, and Reports
PURPOSE AND OBJECTIVES: Collaboration across multiple sectors is needed to bring about health system transformation, but creating effective and sustainable collaboratives is challenging. We describe outcomes and lessons learned from the Hearts of Sonoma County (HSC) initiative, a successful multi-sector collaborative effort to reduce cardiovascular disease (CVD) risk in Sonoma County, California.
INTERVENTION APPROACH: HSC works in both clinical systems and communities to reduce CVD risk. The initiative grew out of a longer-term county-wide collaborative effort known as Health Action. The clinical component involves activating primary care providers around management of CVD risk factors; community activities include community health workers …
Prediction Of Cardiovascular Outcomes With Machine Learning Techniques: Application To The Cardiovascular Outcomes In Renal Atherosclerotic Lesions (Coral) Study., Tian Chen, Pamela Brewster, Katherine Tuttle, Lance D Dworkin, William Henrich, Barbara A Greco, Michael Steffes, Sheldon Tobe, Kenneth Jamerson, Karol Pencina, Joseph M Massaro, Ralph B D'Agostino, Donald E Cutlip, Timothy P Murphy, Christopher J Cooper, Joseph I Shapiro
Prediction Of Cardiovascular Outcomes With Machine Learning Techniques: Application To The Cardiovascular Outcomes In Renal Atherosclerotic Lesions (Coral) Study., Tian Chen, Pamela Brewster, Katherine Tuttle, Lance D Dworkin, William Henrich, Barbara A Greco, Michael Steffes, Sheldon Tobe, Kenneth Jamerson, Karol Pencina, Joseph M Massaro, Ralph B D'Agostino, Donald E Cutlip, Timothy P Murphy, Christopher J Cooper, Joseph I Shapiro
Articles, Abstracts, and Reports
Background: Data derived from the Cardiovascular Outcomes in Renal Atherosclerotic Lesions (CORAL) study were analyzed in an effort to employ machine learning methods to predict the composite endpoint described in the original study.
Methods: We identified 573 CORAL subjects with complete baseline data and the presence or absence of a composite endpoint for the study. These data were subjected to several models including a generalized linear (logistic-linear) model, support vector machine, decision tree, feed-forward neural network, and random forest, in an effort to attempt to predict the composite endpoint. The subjects were arbitrarily divided into training and testing subsets according …