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Utilizing Deep Learning and RDF to Predict Heart Transplantation Survival

Medved, Dennis LU ; Nilsson, Johan LU and Nugues, Pierre LU (2020) 11th International Symposium on Foundations of Information and Knowledge Systems, FoIKS 2020 In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 12012 LNCS. p.175-190
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... (More)

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.

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Please use this url to cite or link to this publication:
author
organization
publishing date
type
Chapter in Book/Report/Conference proceeding
publication status
published
subject
host publication
Foundations of Information and Knowledge Systems : 11th International Symposium, FoIKS 2020, Proceedings - 11th International Symposium, FoIKS 2020, Proceedings
series title
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
editor
Herzig, Andreas ; Kontinen, Juha ; and
volume
12012 LNCS
pages
16 pages
publisher
Springer, Cham
conference name
11th International Symposium on Foundations of Information and Knowledge Systems, FoIKS 2020
conference location
Dortmund, Germany
conference dates
2020-02-17 - 2020-02-21
external identifiers
  • scopus:85080964550
ISSN
0302-9743
1611-3349
ISBN
978-3-030-39951-1
9783030399504
DOI
10.1007/978-3-030-39951-1_11
language
English
LU publication?
yes
id
b45ae608-1278-4701-a393-641206e1da58
date added to LUP
2020-03-20 15:06:59
date last changed
2020-03-24 07:11:47
@inproceedings{b45ae608-1278-4701-a393-641206e1da58,
  abstract     = {<p>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 &amp; 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.</p>},
  author       = {Medved, Dennis and Nilsson, Johan and Nugues, Pierre},
  booktitle    = {Foundations of Information and Knowledge Systems : 11th International Symposium, FoIKS 2020, Proceedings},
  editor       = {Herzig, Andreas and Kontinen, Juha},
  isbn         = {978-3-030-39951-1},
  issn         = {0302-9743},
  language     = {eng},
  month        = {01},
  pages        = {175--190},
  publisher    = {Springer, Cham},
  series       = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
  title        = {Utilizing Deep Learning and RDF to Predict Heart Transplantation Survival},
  url          = {http://dx.doi.org/10.1007/978-3-030-39951-1_11},
  doi          = {10.1007/978-3-030-39951-1_11},
  volume       = {12012 LNCS},
  year         = {2020},
}