Multi-step prediction of pulmonary infection with the use of evolutionary fuzzy cognitive maps

Neurocomputing

Volumn 92, Issue , 2012, Pages 28-35

  Papageorgiou, Elpiniki I ^a   Froelich, Wojciech ^b  

^a TECHNOLOGICAL EDUCATIONAL INSTITUTE OF LAMIA   (Greece)

^b UNIVERSITY OF SILESIA   (Poland)

Author keywords

Fuzzy cognitive maps; Prediction; Pulmonary infection

Indexed keywords

APPLICATION DOMAINS; FUZZY COGNITIVE MAP; MEDICAL DOMAINS; MULTI-STEP; MULTI-STEP PREDICTION; PREDICTIVE MODELS; PULMONARY INFECTIONS; VAGUENESS AND UNCERTAINTY;

FUZZY RULES; FUZZY SYSTEMS;

FORECASTING;

ALGORITHM; ANALYTIC METHOD; ARTICLE; EXPERIMENTAL STUDY; FUZZY COGNITIVE MAP; LUNG INFECTION; MATHEMATICAL ANALYSIS; MATHEMATICAL COMPUTING; MULTI STEP PREDICTION; PNEUMONIA; PREDICTION; PRIORITY JOURNAL; STATISTICAL MODEL; VALIDATION PROCESS;

EID: 84861481357     PISSN: 09252312     EISSN: 18728286     Source Type: Journal    
DOI: 10.1016/j.neucom.2011.08.034     Document Type: Article

References (39)

1. Kosko B. Fuzzy cognitive maps. Int. J. Man-Mach. Stud. 1986, 24(1):65-75.
2. Zadeh L. The concept of a linguistic variable and its application to approximate reasoning. Inf. Sci. 1975, 1:119-249.
3. Liu Z., Satur R. Contextual fuzzy cognitive map for decision support in geographic information systems. IEEE Trans. Fuzz. Syst. 1999, 5:495-507.
4. Stylios C.D., Groumpos P.P. Modeling complex systems using fuzzy cognitive maps. IEEE Trans. Syst. Man Cybern. A 2004, 34(1):155-162.
5. Beena P., Ganguli R. Structural damage detection using fuzzy cognitive maps and Hebbian learning. Appl. Soft Comput. 2011, 11:1014-1020.
6. E.I. Papageorgiou, Ath. Markinos, Th. Gemtos, Soft computing technique of fuzzy cognitive maps to connect yield defining parameters with yield in cotton crop production in Central Greece as a basis for a decision support system for precision agriculture application, in Book: Fuzzy Cognitive Maps: Advances in Theory, Methodologies, Tools, Applications, M. Glykas, (Ed.), Springer Verlag, 2010, pp. 325-362.
7. Salmeron J.L. Augmented fuzzy cognitive maps for modelling LMS critical success factors. Knowl. Based Syst. 2009, 22:275-278.
8. Stylios C., Georgopoulos V. Fuzzy cognitive maps structure for medical decision support systems. Stud. Fuzziness Soft Comput. 2008, 218:151-174.
9. Papageorgiou E.I. A new methodology for decisions in medical informatics using fuzzy cognitive maps based on fuzzy rule-extraction techniques. Appl. Soft Comput. 2011, 11:500-513.
10. Khan M.S., Chong A., Gedeon T.D. A methodology for developing adaptive fuzzy cognitive maps for decision support. J. Adv. Comput. Intell. 2000, 4(6):403-407.
11. Kosko B. Differential Hebbian learning. Neural Networks for Computing, American Institute of Physics 1986, April:277-282.
12. Stach W., Kurgan L., Pedrycz W., Reformat M. Genetic learning of fuzzy cognitive maps. Fuzzy Sets Syst. 2005, 153(3):371-401.
13. Papageorgiou E., Stylios C.D., Groumpos P.P. Active Hebbian learning algorithm to train fuzzy cognitive maps. Int. J. Approx. Reason. 2004, 37(3):219-249.
14. Mateou N.H., Andreou A.S. A framework for developing intelligent decision support systems using evolutionary fuzzy cognitive maps. J. Intell. Fuzzy Syst. 2008, 19:151-170.
15. W. Stach, L.A. Kurgan, W. Pedrycz, A survey of fuzzy cognitive map learning methods, in: P. Grzegorzewski, M. Krawczak, S. Zadrozny, (Eds.), Issues in Soft Computing: Theory and Applications, Publishing House Exit, pp. 71-84, 2005, (ISBN-10: 83-87674-98-2).
16. Froelich W., Juszczuk P. Predictive capabilities of adaptive and evolutionary fuzzy cognitive maps-a comparative study. Intelligent Systems for Knowledge Management of Studies in Computational Intelligence 2009, vol. 252:153-174. Springer.
17. Papakostas G., Boutalis Y., Koulouriotis D., Mertzios B. Fuzzy cognitive maps for pattern recognition applications. Int. J. Pattern Recognition Artif. Intell. 2008, 22(8):1461-1486.
18. Hoare Z., Lim W.S. Pneumonia: update on diagnosis and management. Br. Med. J. 2006, 332(7549):1077-1079. 10.1136/bmj.332.7549.1077.
19. Langer M., Pifferi S., Peta M. Diagnosis of bacterial infection in the ICU: general principles. Intensive Care Med. 1994, 20(4):1232-1238.
20. Wikipedia, 〈〉. http://en.wikipedia.org/wiki/pneumonia_severity_index.
21. Fine M.J., Auble T.E., Yealy D.M., Hanusa B.H., Weissfeld L.A., Singer D.E., Coley C.M., Marrie T.J., Kapoor W.N. A prediction rule to identify low-risk patients with community-acquired pneumonia. N. Engl. J. Med. 1997, 336(4):243-250.
22. Pereira J., Tonelli P., Barros L., Ortega N. Clinical signs of pneumonia in children: association with and prediction of diagnosis by fuzzy sets theory. Braz. J. Med. Biol. Res. 2004, 37(5):701-709.
23. Heckerling P., Gerber B., Tape T., Wigton R. Prediction of community-acquired pneumonia using artificial neural networks. Med. Decision Making 2004, 23(2):112-121.
24. Visweswaran S., Cooper G. Patient-specific models for predicting the outcomes of patients with community acquired pneumonia. AMIA Annual Symposium Proceedings 2005, 759-763.
25. Heckerling P., Gerber B., Tape T., Wigton R. Use of genetic algorithms for neural networks to predict community-acquired pneumonia. Artif. Intell. Med. 2004, 30(1):71-84.
26. Papageorgiou E.I., Papandrianos N., Karagianni G., Kyriazopoulos G., Sfyras D. Fuzzy cognitive map based approach for assessing pulmonary infections. Proeedings of the 18th International Symposium on Foundations of Intelligent Systems, ISMIS '09 2009, 109-118. Springer-Verlag, Berlin, Heidelberg.
27. Zadeh L. Fuzzy sets. Inf. Control 1965, 8:338-353.
28. Dickerson J., Kosko B. Virtual worlds as fuzzy cognitive map. Presence 1994, 3(2):173-189.
29. Pelaez C.E., Bowles J.B. Applying fuzzy cognitive maps knowledge representation to failure modes effects analysis. Proceedings of the Annual Reliability and Maintainability Symposium 1995, 450-456.
30. Papageorgiou E.I., Froelich W. Forecasting the state of pulmonary infection by the application of fuzzy cognitive maps. Proceeding of ITAB'10 2010, 1-4.
31. A.V. Huerga, A balanced differential learning algorithm in fuzzy cognitive maps, in: Proceedings of the 16th International Workshop on Qualitative Reasoning, 2002.
32. Papageorgiou E.I., Stylios C.D., Groumpos P.P. Fuzzy cognitive map learning based on nonlinear Hebbian rule. Australian Conference on Artificial Intelligence 2003, 256-268.
33. Juszczuk P., Froelich W. Learning fuzzy cognitive maps using a differential evolution algorithm. Pol. J. Environ. Stud. 2009, 12(3B):108-112.
34. Stach W., Kurgan L.A., Pedrycz W. Numerical and linguistic prediction of time series with the use of fuzzy cognitive maps. IEEE Trans. Fuzzy Syst. 2008, 16(1):61-72.
35. W. Stach, L.A. Kurgan, W. Pedrycz, Parallel learning of large fuzzy cognitive maps, in: Proceedings of International Joint Conference on Neural Networks, Orlando, FL, 2007, pp. 1-6.
36. Bäck T., Fogel D., Michalewicz Z. Handbook of Evolutionary Computation 1997, Oxford University Press.
37. Dietterich T. Approximate statistical tests for comparing supervised classification learning algorithms. Neural Comput. 1998, 10:1895-1923.
38. Kohavi R. A study of cross-validation and bootstrap for accuracy estimation and model selection. Proceedings of the 14th International Joint Conference on Artificial Intelligence 1995, vol. 2:1137-1143. Morgan Kaufmann.
39. Krzanowski W., Hand D. Assessing error rate estimators: the leave-one-out method reconsidered. Aust. J. Stat. 1997, 39:35-46.