Abstract
In its first part, this contribution reviews shortly the application of neural network methods to medical problems and characterizes its advantages and problems in the context of the medical background. Successful application examples show that human diagnostic capabilities are significantly worse than the neural diagnostic systems. Then, paradigm of neural networks is shortly introduced and the main problems of medical data base and the basic approaches for training and testing a network by medical data are described. Additionally, the problem of interfacing the network and its result is given and the neuro-fuzzy approach is presented. Finally, as case study of neural rule based diagnosis septic shock diagnosis is described, on one hand by a growing neural network and on the other hand by a rule based system.
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References
Baxt W. (1990): Use of an artificial neural network for data analysis in clinical decision making: the diagnosis of acute coronary occlusion, Neural Computation; 2: 480–489
Bottaci L., Drew P., Hartley J., Hadfield M., Farouk R., Lee P., Macintyre I., Duthie G., Monson J. (1997): Artificial neural networks applied to outcome prediction for colorectal cancer patients in separate institutions; Lancet, 350: 469–472
Bounds D., Lloyd P. J.(1988): A multi-layer perceptron network for the diagnosis of low back pain; Proc. IEEE Int. Conf. on Neural Networks, Vol.II: 481–489
Brause R. (1995): Neuronale Netze; Teubner Verlag, Stuttgart, 2nd edition
Brause R., Hamker F., Paetz J.: Septic Shock Diagnosis By Neural Networks And Rule Based Systems; in: L.C. Jain: Computational Intelligence Techniques In Medical Diagnosis And Prognosis, Springer Verlag 2001, in press
Brause R., Hanisch E. (Eds.) (2000), Medical Data Analysis ISMDA 2000. Springer Lecture Notes in Comp.Sc., LNCS 1933, Springer Verlag, Heidelberg
Brause R., Friedrich F. (2000): A Neuro-Fuzzy Approach as Medical Interface, European Symp. On Neural Networks, ESANN 2000, pp. 201–206, D-Facto, Brussels
de Dombal F. T., Leaper D. J., Staniland J. R., McCann A. P., Horrocks J. C. (1972): Acute Abdominal Pain, Brit. Med. J. 2: 9–13
Forsstrom J. J., Dalton K. J. (1995): Artificial neural networks for decision support in clinical medicine. Ann Med. Oct; 27(5):509–17.
Fritzke B. (1995): A Growing Neural Gas Network Learns Topologies, Proc. Advances in Neural Information Processing Systems (NIPS 7), in G. Tesauro, D. S. Touretzky, T. K. Leen, MIT Press, Cambridge, MA, 625–632
Hamker F., Paetz J., Thöne S., Brause R., Hanisch E. (2000): Erkennung kritischer Zustände von Patienten mit der Diagnose “Septischer Schock” mit einem RBF-Netz. Interner Bericht 04/00, Fachbereich Informatik, J. W. Goethe-Universität Frankfurt a. M., http://www.cs.uni-frankfurt.de/fbreports/fbreport04-00.pdf
Haykin S. (1999): Neural Networks, A Comprehensive Foundation. Prentice Hall, 2nd edition, Upper Saddle River, NJ 07458
Heden B., Edenbrandt L., Haisty W. K. jr., Pahlm O. (1994): Artificial neural networks for the electrocardiographic diagnosis of healed myocardial infarction; Am. J. Cardiol. 74(1): 5–8
Heinke D., Hamker F. (1998), Comparing Neural Networks, A Benchmark on Growing Neural Gas, Growing Cell Structures, and Fuzzy ARTMAP. IEEE Transactions on Neural Networks 9(6), 1279–1291
Hornik K., Stinchcombe M., White H. (1990): Universal Approximation of an Unknown Mapping and Its Derivatives Using Multilayer Feedforward Networks; Neural Networks, Vol 3, pp. 551–560
http://www.cs.uni-frankfurt.de/~brause/software/Analyst1.exe
Huber K.-P., Berthold M. R. (1995): Building Precise Classifiers with Automatic Rule Extraction. IEEE International Conference on Neural Networks 3, 1263–1268
Zadeh L. A., Kacprzyk J. (1992): Fuzzy logic for the management of uncertainty, John Wiley & Sons Inc., New York
McGonigal M. (1994): A New Technique for Survival Prediction in Trauma Care Using a Neural Network, Proc. World Conference on Neural Networks, pp.3495–3498
Miller P. L. (1986): Expert Critiquing Systems: Practice-based Medical Consultation by Computer; Springer Verlag, New York
Paetz J., Hamker F., and Thöne S. (2000): About the Analysis of Septic Shock Patient Data, in:6
Penny W., Frost D. (1996): Neural networks in clinical medicine. Med. Decis. Making. Oct–Dec; 16(4):386–398
Sharpe P. K. Solberg H. E. Rootwelt K. Yearworth M. (1993): Artificial neural networks in diagnosis of thyroid function from in vitro laboratory tests; Clin. Chem. 39(11 Pt 1): 2248–2253
Sharpe P. K., Caleb P. (1994): Artificial neural networks within medical decision support systems. Scand J Clin Lab Invest Suppl.;219:3–11.
Snow P. B., Smith D. S. (1994): Artificial neural networks in the diagnosis and prognosis of prostate cancer: a pilot study; J. Urology. 152(5 Pt 2): 1923–1926
Wade, S., Büssow, M. Hanisch, E. (1998): Epidemiology of Systemic Inflammatory Response Syndrome, Sepsis and Septic Shock in Surgical Intensive Care Patients, Chirurg;69(6):648–655
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Brause, R.W. (2001). Medical Analysis and Diagnosis by Neural Networks. In: Crespo, J., Maojo, V., Martin, F. (eds) Medical Data Analysis. ISMDA 2001. Lecture Notes in Computer Science, vol 2199. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45497-7_1
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DOI: https://doi.org/10.1007/3-540-45497-7_1
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