Improving risk prediction in heart failure using machine learning-Reference-Cited by-同舟云学术

Improving risk prediction in heart failure using machine learning

Published:2019-11-12 Issue:1 Volume:22 Page:139-147
ISSN:1388-9842
Container-title:European Journal of Heart Failure
language:en
Short-container-title:Eur J Heart Fail

Author:

Adler Eric D.¹,Voors Adriaan A.²,Klein Liviu³,Macheret Fima⁴,Braun Oscar O.⁵,Urey Marcus A.¹,Zhu Wenhong⁴,Sama Iziah²,Tadel Matevz⁶,Campagnari Claudio⁷,Greenberg Barry¹,Yagil Avi¹⁶

Affiliation:

1. Division of Cardiology, Department of MedicineUC San Diego La Jolla CA USA

2. University of Groningen, University Medical Center Groningen Groningen The Netherlands

3. Division of Cardiology, Department of MedicineUC San Francisco San Francisco CA USA

4. Altman Clinical and Translational Research Institute (ACTRI), UC San Diego La Jolla CA USA

5. Cardiology, Department of Clinical SciencesLund University and Skåne University Hospital Lund Sweden

6. Physics DepartmentUC San Diego La Jolla CA USA

7. Physics DepartmentUC Santa Barbara Santa Barbara CA USA

Funder

European Commission

Publisher

Wiley

Subject

Cardiology and Cardiovascular Medicine

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1002/ejhf.1628

Reference30 articles.

1. Utility of Amino-Terminal Pro–Brain Natriuretic Peptide Testing for Prediction of 1-Year Mortality in Patients With Dyspnea Treated in the Emergency Department

2. The Prognostic Value of N-Terminal Pro–B-Type Natriuretic Peptide for Death and Cardiovascular Events in Healthy Normal and Stage A/B Heart Failure Subjects

3. Risk Stratification for In-Hospital Mortality in Acutely Decompensated Heart FailureClassification and Regression Tree Analysis

4. The Seattle Heart Failure Model

5. Usefulness of the INTERMACS Scale to Predict Outcomes After Mechanical Assist Device Implantation

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