Interpatient Similarities in Cardiac Function: A Platform for Personalized Cardiovascular Medicine.

Journal: JACC. Cardiovascular imaging

Published Date: Mar 18, 2020

Abstract

OBJECTIVES: The authors applied unsupervised machine-learning techniques for integrating echocardiographic features of left ventricular (LV) structure and function into a patient similarity network that predicted major adverse cardiac event(s) (MACE) in an individual patient.

Authors

Márton Tokodi

Division of Cardiology, West Virginia University Heart & Vascular Institute, Morgantown, West Virginia.
Sirish Shrestha

Division of Cardiology, WVU Heart & Vascular Institute, West Virginia University, Morgantown, West Virginia.
Christopher Bianco

Division of Cardiology, West Virginia University Heart & Vascular Institute, Morgantown, West Virginia.
Nobuyuki Kagiyama

West Virginia University Heart and Vascular Institute Morgantown WV.
Grace Casaclang-Verzosa

Division of Cardiology, West Virginia University Heart & Vascular Institute, Morgantown, West Virginia.
Jagat Narula
Partho P Sengupta

Division of Cardiovascular Diseases and Hypertension, Robert Wood Johnson University Hospital, and Rutgers Robert Wood Johnson Medical School, New Brunswick, New Jersey, USA.

Keywords

Adult Aged Aged, 80 and over Disease Progression Echocardiography Female Humans Image Interpretation, Computer-Assisted Male Middle Aged Patient Readmission Pattern Recognition, Automated Predictive Value of Tests Prognosis Prospective Studies Registries Retrospective Studies Unsupervised Machine Learning Ventricular Dysfunction, Left Ventricular Function, Left

External Resources

View on PubMed Access via DOI PubMed (32199835)

Interpatient Similarities in Cardiac Function: A Platform for Personalized Cardiovascular Medicine.

Abstract

Authors

Keywords

External Resources

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