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.
This research was supported by Heart Lung Foundation, The Swedish Research Council, and the eSSENCE program.
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References
Barzdins, G., Rikacovs, S., Veilande, M., Zviedris, M.: Ontological re-engineering of medical databases. In: Proceedings of the Latvian Academy of Sciences, vol. 63, p. 156. De Gruyter Open Sp. z oo (2009)
Baxt, W.G.: Application of artificial neural networks to clinical medicine. Lancet 346(8983), 1135–1138 (1995)
Boulos, M.N.K., Roudsari, A.V., Carson, E.R.: Towards a semantic medical web: Healthcybermap’s tool for building an RDF metadata base of health information resources based on the qualified Dublin core metadata set. Med. Sci. Monit. 8(7), MT124–MT126 (2002)
Bratsas, C., Quaresma, P., Pangalos, G., Maglaveras, N.: Using ontologies to build a knowledge base of cardiology problems and algorithms. In: Computers in Cardiology, 2004, pp. 609–612. IEEE (2004)
Cucchetti, A., et al.: Artificial neural network is superior to meld in predicting mortality of patients with end-stage liver disease. Gut 56(2), 253–258 (2007)
Dieng-Kuntz, R., Minier, D., Ruzicka, M., Corby, F., Corby, O., Alamarguy, L.: Building and using a medical ontology for knowledge management and cooperative work in a health care network. Comput. Biol. Med. 36(7–8), 871–892 (2006)
Domingos, P.: A few useful things to know about machine learning. Commun. ACM 55(10), 78–87 (2012). https://doi.org/10.1145/2347736.2347755. http://doi.acm.org/10.1145/2347736.2347755
Ham, K.: Openrefine (version 2.5). http://openrefine.org. Free, open-source tool for cleaning and transforming data. J. Med. Libr. Assoc. JMLA 101(3), 233 (2013)
Lindemann, G., Schmidt, D., Schrader, T., Keune, D.: The resource description framework (RDF) as a modern structure for medical data. Int. J. Biol. Med. Sci. 4(2) (2009)
Medved, D., Nugues, P., Nilsson, J.: Selection of an optimal feature set to predict heart transplantation outcomes. In: 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3290–3293, August 2016
Medved, D., Nugues, P., Nilsson, J.: Predicting the outcome for patients in a heart transplantation queue using deep learning. In: 2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 74–77. IEEE (2017)
Medved, D., Nugues, P., Nilsson, J.: Simulating the outcome of heart allocation policies using deep neural networks. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). IEEE (2018, to appear)
Medved, D., Ohlsson, M., Höglund, P., Andersson, B., Nugues, P., Nilsson, J.: Improving prediction of heart transplantation outcome using deep learning techniques. Sci. Rep. 8(1), 3613 (2018)
Nilsson, J., Ohlsson, M., Höglund, P., Ekmehag, B., Koul, B., Andersson, B.: Artificial neural networks - relative importance of different recipient-donor characteristic combinations on survival after heart transplantation. J. Hear. Lung Transplant. 30, S68 (2011)
Nilsson, J., Ohlsson, M., Höglund, P., Ekmehag, B., Koul, B., Andersson, B.: The international heart transplant survival algorithm (IHTSA): a new model to improve organ sharing and survival. PLoS ONE 10(3), e0118644 (2015)
Riaño, D., Real, F., López-Vallverdú, J.A., Campana, F., Ercolani, S., Mecocci, P., Annicchiarico, R., Caltagirone, C.: An ontology-based personalization of health-care knowledge to support clinical decisions for chronically ill patients. J. Biomed. Inform. 45(3), 429–446 (2012)
Weiss, E.S., et al.: Creation of a quantitative recipient risk index for mortality prediction after cardiac transplantation (IMPACT). Ann. Thorac. Surg. 92(3), 914–922 (2011). https://doi.org/10.1016/j.athoracsur.2011.04.030. http://www.sciencedirect.com/science/article/pii/S0003497511009350
WWWC: RDF 1.1 concepts and abstract syntax (2014). https://www.w3.org/TR/rdf11-concepts/
Acknowledgements
This work is based on OPTN data as of October 1, 2013 and was supported in part by the Health Resources and Services Administration contract 234-2005-370011C. The content is the responsibility of the authors alone and does not necessarily reflect the views or policies of the Department of Health and Human Services, nor does mention of trade names, commercial products, or organizations imply endorsement by the U.S. Government. This research was supported by Heart Lung Foundation, The Swedish Research Council, and the eSSENCE program.
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Medved, D., Nilsson, J., Nugues, P. (2020). Utilizing Deep Learning and RDF to Predict Heart Transplantation Survival. In: Herzig, A., Kontinen, J. (eds) Foundations of Information and Knowledge Systems. FoIKS 2020. Lecture Notes in Computer Science(), vol 12012. Springer, Cham. https://doi.org/10.1007/978-3-030-39951-1_11
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