Webbläsaren som du använder stöds inte av denna webbplats. Alla versioner av Internet Explorer stöds inte längre, av oss eller Microsoft (läs mer här: * https://www.microsoft.com/en-us/microsoft-365/windows/end-of-ie-support).

Var god och använd en modern webbläsare för att ta del av denna webbplats, som t.ex. nyaste versioner av Edge, Chrome, Firefox eller Safari osv.

Foto på Mattias Ohlsson

Mattias Ohlsson

Professor

Foto på Mattias Ohlsson

Improving prediction of heart transplantation outcome using deep learning techniques

Författare

  • Dennis Medved
  • Mattias Ohlsson
  • Peter Höglund
  • Bodil Andersson
  • Pierre Nugues
  • Johan Nilsson

Summary, in English

The primary objective of this study is to compare the accuracy of two risk models, International Heart Transplantation Survival Algorithm (IHTSA), developed using deep learning technique, and Index for Mortality Prediction After Cardiac Transplantation (IMPACT), to predict survival after heart transplantation. Data from adult heart transplanted patients between January 1997 to December 2011 were collected from the UNOS registry. The study included 27,860 heart transplantations, corresponding to 27,705 patients. The study cohorts were divided into patients transplanted before 2009 (derivation cohort) and from 2009 (test cohort). The receiver operating characteristic (ROC) values, for the validation cohort, computed for one-year mortality, were 0.654 (95% CI: 0.629–0.679) for IHTSA and 0.608 (0.583–0.634) for the IMPACT model. The discrimination reached a C-index for long-term survival of 0.627 (0.608–0.646) for IHTSA, compared with 0.584 (0.564–0.605) for the IMPACT model. These figures correspond to an error reduction of 12% for ROC and 10% for C-index by using deep learning technique. The predicted one-year mortality rates for were 12% and 22% for IHTSA and IMPACT, respectively, versus an actual mortality rate of 10%. The IHTSA model showed superior discriminatory power to predict one-year mortality and survival over time after heart transplantation compared to the IMPACT model.

Avdelning/ar

  • Institutionen för datavetenskap
  • Artificiell intelligens och thoraxkirurgisk vetenskap (AICTS)
  • eSSENCE: The e-Science Collaboration
  • ELLIIT: the Linköping-Lund initiative on IT and mobile communication
  • Beräkningsbiologi och biologisk fysik - Har omorganiserats
  • Institutionen för astronomi och teoretisk fysik - Har omorganiserats
  • Avdelningen för klinisk kemi och farmakologi
  • Kliniska studier vid kronisk njursjukdom (CKD)
  • Proteasinhibitorforskning
  • Thoraxkirurgi
  • Hjärt- och lungtransplantation
  • Kirurgi, Lund
  • Lever-, pankreas- och gallvägskirurgi
  • Robotik och Semantiska System

Publiceringsår

2018-02-26

Språk

Engelska

Publikation/Tidskrift/Serie

Scientific Reports

Issue

8

Dokumenttyp

Artikel i tidskrift

Förlag

Nature Publishing Group

Ämne

  • Other Computer and Information Science
  • Cardiac and Cardiovascular Systems
  • Surgery

Status

Published

Projekt

  • Lund University AI Research

Forskningsgrupp

  • Artificial Intelligence in CardioThoracic Sciences (AICTS)
  • Clinical studies in CKD
  • Protease Inhibitor Research
  • Heart and Lung transplantation
  • Hepato-Pancreato-Biliary Surgery

ISBN/ISSN/Övrigt

  • ISSN: 2045-2322