NDLI: A dynamic Bayesian network approach for time-specific survival probability prediction in patients after ventricular assist device implantation

Content Provider	IEEE Xplore Digital Library
Author	Exarchos, T.P. Rigas, G. Goletsis, Y. Stefanou, K. Jacobs, S. Trivella, M.-G. Fotiadis, D.I.
Copyright Year	2014
Description	Author affiliation: Dept. of Biomed. Res., FORTH Univ. of Ioannina, Ioannina, Greece (Exarchos, T.P.; Rigas, G.; Stefanou, K.) \|\| Inst. of Clinical Physiol., Pisa, Italy (Trivella, M.-G.) \|\| Dept. of Econ., Univ. of Ioannina, Ioannina, Greece (Goletsis, Y.) \|\| Dept. of Cardiac Surg., Univ. Hosp. Leuven, Leuven, Belgium (Jacobs, S.) \|\| Dept. of Mater. Sci. & Eng., Univ. of Ioannina, Ioannina, Greece (Fotiadis, D.I.)
Abstract	In this work we present a decision support tool for the calculation of time-dependent survival probability for patients after ventricular assist device implantation. Two different models have been developed, a short term one which predicts survival for the first three months and a long term one that predicts survival for one year after implantation. In order to model the time dependencies between the different time slices of the problem, a dynamic Bayesian network (DBN) approach has been employed. DBNs order to capture the temporal events of the patient disease and the temporal data availability. High accuracy results have been reported for both models. The short and long term DBNs reached an accuracy of 96.97% and 93.55% respectively.
Sponsorship	IEEE Eng. Med. Biol. Soc.
Starting Page	3172
Ending Page	3175
File Size	911352
Page Count	4
File Format	PDF
ISBN	9781424479290
ISSN	1557170X
DOI	10.1109/EMBC.2014.6944296
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-08-26
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Heart Bayes methods Accuracy Biological system modeling Diseases Medical treatment Lungs
Content Type	Text
Resource Type	Article
Subject	Signal Processing Biomedical Engineering Health Informatics Computer Vision and Pattern Recognition

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