Neural networks for the prediction of spirometric reference values

Medical Informatics and the Internet in Medicine

Volumn 28, Issue 4, 2003, Pages 299-309

  Botsis, Taxiarhis ^a,b   Halkiotis, S ^a  

^a UNIVERSITY OF ATHENS   (Greece)

^b NONE   (Greece)

Author keywords

Neural networks; Prediction; Reference values; Spirometry

Indexed keywords

ADULT; AGED; ARTICLE; ARTIFICIAL NEURAL NETWORK; CALCULATION; CONTROLLED STUDY; CORRELATION ANALYSIS; DIAGNOSTIC PROCEDURE; FEMALE; FORCED EXPIRATORY VOLUME; GREECE; HUMAN; LUNG DISEASE; LUNG FUNCTION TEST; MALE; PREDICTION; REFERENCE VALUE; SPIROMETRY; TRAINING;

AGED; FEMALE; GREECE; HUMANS; MALE; MIDDLE AGED; NEURAL NETWORKS (COMPUTER); REFERENCE VALUES; SPIROMETRY;

EID: 84860394685     PISSN: 14639238     EISSN: None     Source Type: Journal    
DOI: 10.1080/14639230310001621701     Document Type: Article

References (31)

1. ACCP, 1983, Scientific section recommendations, statement on spirometry. Chest 3, 547-550.
2. 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.
3. 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.
4. 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.
5. 1 in the Greek elderly. Lung, 178, 201-212.
6. American Thoracic Society, 1991, Lung function testing: selection of reference values and interpretative strategies, American Review of Respiratory Diseases, 144, 1202-1218.
7. American Thoracic Society, 1991, Standardization of spirometry. American Journal of Respiratory Critical Care Medicine, 152, 1107-1136.
8. Haykin, S. 1999, Neural Networks-A comprehensive foundation, 2nd edn (New Jersey; Prentice Hall International).
9. 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.
10. 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.
11. 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.
12. 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.
13. 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.
14. 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.
15. 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.
16. Kasabov, N. K., 1996, Foundations of neural networks, fuzzy systems, and knowledge, 1st edn (Massachusetts: Massachusetts Institute of Technology).
17. Rumelhart, D. E., Hinton, G. E. and Williams, R. J., 1986, Parallel Distributed Processing, 1st edn (Cambridge: MIT Press).
18. Elman, J. L., 1990, Finding structure in time. Cognitive Science, 14, 179-211.
19. 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.
20. 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.
21. 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.
22. 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.
23. 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.
24. 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.
25. 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.
26. 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.
27. 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.
28. Ripley, B. D., 1996, Pattern recognition and Neural Networks, 1st edn (Cambridge; Cambridge University Press).
29. Gallant, S. I., 1990, Perceptron-based learning algorithms. IEEE Transactions on Neural Networks, 1, 179-191.
30. Fahlman, S. E. and Lebiere, C., 1990, The cascade-correlation learning architecture. In NIPS2. edited by D. S. Toupetzsky (San Mateo: Morgan Kaufmann) pp. 524-532.
31. Lumb, A. B., 2000, Nunn's applied respiratory physiology, 5th edn (London: Reed Educational and Professional Publishing Ltd).