Neural networks for the prediction of spirometric reference values

References

• ACCP, 1983, Scientific section recommendations, statement on spirometry. Chest 3, 547– 550.
   Google Scholar
• ENRIGHT, P., KRONMAL, R., HIGGIS M., SCHENKER, M. and HAPONIK, E. F., 1993, Spirometry reference values for women and men 65 to 85 years of age, Cardiovascular Health Study. American Review of Respiratory Diseases, 147, 125 – 133.
   Google Scholar
• QUANJER, H., TAMMELING, G. J., GOTES, J. E., PEDERSEN, O. F., PESLIN, R. and YERNAULT, J. C., 1993, Lung volumes and forced ventilatory flows, report working party standardization of lung function tests, European Community for steel and coal, official statement of the European respiratory society. European Respiratory Journal, 6(suppl 16), 5 – 40.
   Google Scholar
• CRAPO, R.,MORRIS, A. and GARDNER, R., 1981, Reference spirometric values using techniques and equipment that meet ATS recommendations. American Review of Respiratory Diseases, 123, 659– 664.
   PubMed Web of Science ®Google Scholar
• BALTOPOULOS, G.,FILDISIS, G.,KARATZAS, S.,GEORGIAKODIS,F. and MYRIANTHEFS, P., 2000, Reference values and prediction equations for FVC and FEV1 in the Greek elderly. Lung, 178, 201– 212.
   PubMed Web of Science ®Google Scholar
• American Thoracic Society, 1991, Lung function testing: selection of reference values and interpretative strategies, American Review of Respiratory Diseases, 144, 1202– 1218.
   PubMed Web of Science ®Google Scholar
• American Thoracic Society, 1991, Standardization of spirometry. American Journal of Respiratory Critical Care Medicine, 152, 1107– 1136.
   Google Scholar
• HAYKIN, S. 1999, Neural Networks-A comprehensive foundation, 2nd edn (New Jersey; Prentice Hall International).
   Google Scholar
• SILVA, R. and ROQUE DA SILVA, A. C., 1998, Medical diagnosis as a neural networks pattern classification problem. Proceedings of the Third International Conference on Neural Networks and Expert Systems in Medicine and Healthcare, NNESMED’98, Pisa. September 1998.
   Google Scholar
• WEST, D. and WEST, V., 2000, Model selection for a medical diagnostic decision support system: a breast cancer detection case. Artificial Intelligence in Medicine, 20, 183– 204.
   PubMed Web of Science ®Google Scholar
• PIZZI, F., SICURELLO, R. and VARINI, G., 1998, Development of an inductive self-organizing network for the real-time segmentation of diagnostic images. Proceedings of the Third International Conference on Neural Networks and Expert Systems in Medicine and Healthcare, NNESMED’98, Pisa. September 1998.
   Google Scholar
• BIGAN, C., 1998, A recursive time-frequency processing method for neural networks recognition of EEG seizure. Proceedings of the Third International Conference on Neural Networks and Expert Systems in Medicine and Healthcare, NNESMED’98, Pisa. September 1998.
   Google Scholar
• CRISTEA, A., ZAHARIA, C. N. and CIUCA, I., 1998, Simulation of a micropopulational epidemic evolution using a special neural network. Proceedings of the Third International Conference on Neural Networks and Expert Systems in Medicine and Healthcare, NNESMED’98, Pisa. September 1998.
   Google Scholar
• PAPAROUDAKIS, G. M., GROSS, H. G. and ALEXAKIS, I., 1998, Detection of glaucomatic nerve damage using artificial neural networks. Proceedings of the Third International Conference on Neural Networks and Expert Systems in Medicine and Healthcare, NNESMED’98, Pisa. September 1998.
   Google Scholar
• MARKOPOULOS, C., KOUSKOS, E., KOUFOPOULOS, K., KYRIAKOU, V. and GOGAS, J., 2001, Use of artificial neural networks (computer analysis) in the diagnosis of microcalcifications on mammography. European Journal of Radiology, 39, 60– 65.
   PubMed Web of Science ®Google Scholar
• KASABOV, N. K., 1996, Foundations of neural networks, fuzzy systems, and knowledge, 1st edn (Massachusetts: Massachusetts Institute of Technology).
   Google Scholar
• RUMELHART, D. E., HINTON, G. E. and WILLIAMS, R. J., 1986, Parallel Distributed Processing, 1st edn (Cambridge: MIT Press).
   Google Scholar
• ELMAN, J. L., 1990, Finding structure in time. Cognitive Science, 14, 179– 211.
   Web of Science ®Google Scholar
• ELMAN, J. L., 1998, Generalization, simple recurrent networks, and the emergence of structure. Proceedings of the 20th Annual Conference of the Cognitive Science Society, CogSci’98, Wisconsin, August 1998.
   Google Scholar
• AMINZADEH, F., BARHEN, J., Glover, C. W. and TOOMARIAN, N. B., 2000, Reservoir parameter estimation using a hybrid neural network. Computers & Geosciences, 26, 869– 875.
   Web of Science ®Google Scholar
• ELLENIUS, J. and GROTH, T., 2000, Methods for selection of adequate neural network structures with application to early assessment of chest pain patients by biochemical monitoring. International Journal of Medical Informatics, 57, 181– 202.
   PubMed Web of Science ®Google Scholar
• ZAKARYA, D., CHASTRETTE, M., TOLLABI,M. and FKIH-TETOUANI, S., 1999, Structure – camphor odour relationships using the generation and selection of pertinent descriptors approach. Chemometrics and Intelligent Laboratory Systems, 48, 35– 46.
   Web of Science ®Google Scholar
• HONG, T. and HAN, I., 2002, Knowledge-based data mining of news information on the Internet using cognitive maps and neural networks. Experts Systems with Applications, 23, 1–8.
   Web of Science ®Google Scholar
• DAVALOS, S., GRITTA, R. D. and CHOW, G., 1999, The application of neural network approach to predicting bankruptcy risks facing the major US air carriers: 1979 – 1996. Journal of Air Transport Management, 5, 81– 86.
   Google Scholar
• JOHN, C. H. St., BALAKRISHNAN, N. and FIET, J. O., 2002, Modeling the relationship between corporate strategy and wealth creation using neural networks. Computers & Operations Research, 27, 1077– 1092.
   Web of Science ®Google Scholar
• DOUMPOS, M. and ZOPOUNIDIS, C., 1999, A multicriteria discrimination method for the prediction of financial distress: the case of Greece. Multinational Finance Journal, 2, 71 – 101, 3.
   Google Scholar
• DIMITRAS, A. I., SLOWINSKI, R., SUSMAGA, R. and ZOPOUNIDIS, C., 1999, Business failure prediction using rough sets. European Journal of Operational Research, 114, 263– 280.
   Web of Science ®Google Scholar
• RIPLEY, B. D., 1996, Pattern recognition and Neural Networks, 1st edn (Cambridge; Cambridge University Press).
   Google Scholar
• GALLANT, S. I., 1990, Perceptron-based learning algorithms. IEEE Transactions on Neural Networks, 1, 179– 191.
   PubMedGoogle Scholar
• FAHLMAN, S. E. and LEBIERE, C., 1990, The cascade-correlation learning architecture. In <I>NIPS2</I>. edited by D. S. Toupetzsky (San Mateo: Morgan Kaufmann) pp. 524 – 532.
   Google Scholar
• LUMB, A. B., 2000, Nunn’s applied respiratory physiology, 5th edn (London: Reed Educational and Professional Publishing Ltd).
   Google Scholar