TY - GEN
T1 - Utilizing Deep Learning and RDF to Predict Heart Transplantation Survival
AU - Medved, Dennis
AU - Nilsson, Johan
AU - Nugues, Pierre
PY - 2020/1/3
Y1 - 2020/1/3
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85080964550&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-39951-1_11
DO - 10.1007/978-3-030-39951-1_11
M3 - Paper in conference proceeding
AN - SCOPUS:85080964550
SN - 9783030399504
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 175
EP - 190
BT - Foundations of Information and Knowledge Systems
A2 - Herzig, Andreas
A2 - Kontinen, Juha
PB - Springer
CY - Switzerland
T2 - 11th International Symposium on Foundations of Information and Knowledge Systems, FoIKS 2020
Y2 - 17 February 2020 through 21 February 2020
ER -