Machine learning algorithms estimating prognosis and guiding therapy in adult congenital heart disease: data from a single tertiary centre including 10 019 patients-Reference-Cited by-同舟云学术

Machine learning algorithms estimating prognosis and guiding therapy in adult congenital heart disease: data from a single tertiary centre including 10 019 patients

Published:2019-01-26 Issue:13 Volume:40 Page:1069-1077
ISSN:0195-668X
Container-title:European Heart Journal
language:en
Short-container-title:

Author:

Diller Gerhard-Paul¹²³⁴,Kempny Aleksander¹²,Babu-Narayan Sonya V¹²,Henrichs Marthe³,Brida Margarita¹⁵,Uebing Anselm¹⁶,Lammers Astrid E⁶,Baumgartner Helmut³⁴,Li Wei¹²,Wort Stephen J¹²,Dimopoulos Konstantinos¹²,Gatzoulis Michael A¹²

Affiliation:

1. Adult Congenital Heart Centre, National Centre for Pulmonary Hypertension, Royal Brompton Hospital, Sydney Street, London, UK

2. National Heart and Lung Institute, Imperial College School of Medicine, Dovehouse Street, London, UK

3. Department of Cardiology III – Adult Congenital and Valvular Heart Disease, University Hospital Muenster, Albert-Schweitzer-Campus 1, Muenster, Germany

4. Competence Network for Congenital Heart Defects, DZHK (German Centre for Cardiovascular Research), Augustenburger Platz 1, Berlin, Germany

5. Division of Valvular Heart Disease and Adult Congenital Heart Disease, Department of Cardiovascular Medicine, University Hospital Centre Zagreb, Kispaticeva 12, Zagreb, Croatia

6. Division of Paediatric Cardiology, University Hospital Muenster, Albert-Schweitzer-Campus 1, Muenster, Germany

Funder

G.-P.D.

A.K.

K.D.

M.A.G.

Adult Congenital Heart Centre and Centre

Royal Brompton Hospital

Actelion UK

Pfizer

GSK

British Heart Foundation