DESIGN OF THE ARTIFICIAL NEURAL NETWORK MODEL FOR THE PREDICTION OF OUTCOME AFTER STROKE

Jiri Polivka Jr., Petr Kratochvil, Vladimir Rohan, Jiri Polivka, Jana Kleckova

2012

Abstract

In our contemporary research we are trying to develop the artificial neural network (ANN) model for the prediction of outcome after the occurrence of stroke. This paper mentions some important facts about stroke as well as the urgent need for Computer Assisted Decision Support (CAMS) systems in the relation to clinical practice. The short review of related studies of ANN in medicine is included. The model input and output parameters were selected and are also described. The basic ANN design for the predictive model is mentioned together with the future directions of our research.

References

  1. Adams H. P. Jr., Bendixen B. H., Kappelle L. J., Biller J., Love B. B., Gordon D. L., Marsh E. E., 3rd, 1993. Classification of subtype of acute ischemic stroke: definitions for use in a multicenter clinical trial. TOAST. Trial of Org 10172 in Acute Stroke Treatment. Stroke 1993;24:35- 41.
  2. Celler, B. G., Chazal, P., 1998. Low computational cost classifiers for ECG diagnosis using neural networks. Proceedings of the International Conference of Engineering in Medicine & Biology Society (EMBC 1998), pp. 1337-1340.
  3. Elmer Andres Fernández, Rodolfo Valtuille, Jesus Rodriguez Presedo, and Peter Willshaw (2005) Comparison of Standard and Artificial Neural Network Estimators of Hemodialysis Adequacy. International Center for Artificial Organs and Transplantation. 29(2):159-165, Blackwell Publishing, Inc.
  4. Goldstein L. B., Samsa G. P. Reliability of the National Institutes of Health Stroke Scale: extensit to nonneurologists in the context of a clinical trial. Stroke 1997;28:307-10.
  5. Hacke W., Kaste M., Bluhmki E., Brozman M., Dá valos A., Guidetti D. et al. Thrombolysis with alteplase 3 to 4.5 hours after acute ischemic stroke. N Engl J Med 2008;359:1317-29.
  6. Ham, P. M. and Han, S., "Classification of cardiac arrhythmias using fuzzy artmap" IEEE Transactions on Biomedical Engineering, 43(4): 425-430 (1996).
  7. Lloyd-Jones D., Adams R., Carnethon M., De Simone G., Ferguson T. B., Flegal K., et al. Heart disease and stroke statistics-2009 update: a report from the American Heart Association Statistics Committee and Stroke Statistics Subcommittee. Circulation 2009;119:e21-181.
  8. Modai, I., Israel, A., Mendel, S., Hines, E.L. and Weizman, R., "Neural network based on adaptive resonance theory as compared to experts in suggesting treatment for schizophrenic and unipolar depressed in patients," Journal of Medical Systems, 20(6): 403-412 (1996).
  9. Morgenstern L. B., Lisabeth L. D., Mecozzi A. C., Smith M. A., Longwell P. J., McFarling D. A., Risser J. M. A population-based study of acute stroke and TIA diagnosis. Neurology 2004;62:895-900.
  10. Nazeran, H., Behbehani, K., 2001. Neural networks in processing and analysis of biomedical signals. In M. Akay (Ed.), Nonlinear biomedical signal processing: Fuzzy logic, neural networks and new algorithms, pp. 69-97.
  11. Papaloukas, C.,Fotiadis, D. I.,Likas, A., and Michalis, L. K., 2002. An ischemia detection method based on artificial neural networks. Artificial Intelligence in Medicine, 24, 167- 178.
  12. Shanthi, D., Sahoo, G. and Saravanan, N., 2010. Designing an Artificial Neural Network Model for the Prediction of Thrombo-embolic Stroke, International Journals of Biometric and Bioinformatics (IJBB), Volume (3)
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Paper Citation


in Harvard Style

Polivka Jr. J., Kratochvil P., Rohan V., Polivka J. and Kleckova J. (2012). DESIGN OF THE ARTIFICIAL NEURAL NETWORK MODEL FOR THE PREDICTION OF OUTCOME AFTER STROKE . In Proceedings of the International Conference on Health Informatics - Volume 1: HEALTHINF, (BIOSTEC 2012) ISBN 978-989-8425-88-1, pages 467-470. DOI: 10.5220/0003875304670470


in Bibtex Style

@conference{healthinf12,
author={Jiri Polivka Jr. and Petr Kratochvil and Vladimir Rohan and Jiri Polivka and Jana Kleckova},
title={DESIGN OF THE ARTIFICIAL NEURAL NETWORK MODEL FOR THE PREDICTION OF OUTCOME AFTER STROKE},
booktitle={Proceedings of the International Conference on Health Informatics - Volume 1: HEALTHINF, (BIOSTEC 2012)},
year={2012},
pages={467-470},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0003875304670470},
isbn={978-989-8425-88-1},
}


in EndNote Style

TY - CONF
JO - Proceedings of the International Conference on Health Informatics - Volume 1: HEALTHINF, (BIOSTEC 2012)
TI - DESIGN OF THE ARTIFICIAL NEURAL NETWORK MODEL FOR THE PREDICTION OF OUTCOME AFTER STROKE
SN - 978-989-8425-88-1
AU - Polivka Jr. J.
AU - Kratochvil P.
AU - Rohan V.
AU - Polivka J.
AU - Kleckova J.
PY - 2012
SP - 467
EP - 470
DO - 10.5220/0003875304670470