Utilizing Deep Learning and RDF to Predict Heart Transplantation Survival

Research output: Chapter in Book/Report/Conference proceedingPaper in conference proceeding

Abstract

In this paper, we describe the conversion of three different heart transplantation data sets to a Resource Description Framework (RDF) representation and how it can be utilized to train deep learning models. These models were used to predict the outcome of patients both pre- and post-transplant and to calculate their survival time. The International Society for Heart & Lung Transplantation (ISHLT) maintains a registry of heart transplantations that it gathers from grafts performed worldwide. The American organization United Network for Organ Sharing (UNOS) and the Scandinavian Scandiatransplant are contributors to this registry, although they use different data models. We designed a unified graph representation covering these three data sets and we converted the databases into RDF triples. We used the resulting triplestore as input to several machine learning models trained to predict different aspects of heart transplantation patients. Recipient and donor properties are essential to predict the outcome of heart transplantation patients. In contrast with the manual techniques we used to extract data from the tabulated files, the RDF triplestore together with SPARQL, enables us to experiment quickly and automatically with different combinations of features sets, to predict the survival, and simulate the effectiveness of organ allocation policies.

Details

Authors
Organisations
External organisations
  • Skåne University Hospital
Research areas and keywords

Subject classification (UKÄ) – MANDATORY

  • Bioinformatics (Computational Biology)
  • Cardiac and Cardiovascular Systems
Original languageEnglish
Title of host publicationFoundations of Information and Knowledge Systems
Subtitle of host publication11th International Symposium, FoIKS 2020, Proceedings
EditorsAndreas Herzig, Juha Kontinen
Place of PublicationSwitzerland
PublisherSpringer, Cham
Pages175-190
Number of pages16
ISBN (Electronic)978-3-030-39951-1
ISBN (Print)9783030399504
Publication statusPublished - 2020 Jan 3
Publication categoryResearch
Peer-reviewedYes
Event11th International Symposium on Foundations of Information and Knowledge Systems, FoIKS 2020 - Dortmund, Germany
Duration: 2020 Feb 172020 Feb 21

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume12012 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference11th International Symposium on Foundations of Information and Knowledge Systems, FoIKS 2020
CountryGermany
CityDortmund
Period2020/02/172020/02/21