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

Kosko, 1986, Fuzzy cognitive maps, Int. J. Man–Mach. Stud., 24, 65, 10.1016/S0020-7373(86)80040-2 Zadeh, 1975, The concept of a linguistic variable and its application to approximate reasoning, Inf. Sci., 1, 119 Liu, 1999, Contextual fuzzy cognitive map for decision support in geographic information systems, IEEE Trans. Fuzz. Syst., 5, 495 Stylios, 2004, Modeling complex systems using fuzzy cognitive maps, IEEE Trans. Syst. Man Cybern. A, 34, 155, 10.1109/TSMCA.2003.818878 Beena, 2011, Structural damage detection using fuzzy cognitive maps and Hebbian learning, Appl. Soft Comput., 11, 1014, 10.1016/j.asoc.2010.01.023 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. Salmeron, 2009, Augmented fuzzy cognitive maps for modelling LMS critical success factors, Knowl. Based Syst., 22, 275, 10.1016/j.knosys.2009.01.002 Stylios, 2008, Fuzzy cognitive maps structure for medical decision support systems, Stud. Fuzziness Soft Comput., 218, 151, 10.1007/978-3-540-73185-6_7 Papageorgiou, 2011, A new methodology for decisions in medical informatics using fuzzy cognitive maps based on fuzzy rule-extraction techniques, Appl. Soft Comput., 11, 500, 10.1016/j.asoc.2009.12.010 Khan, 2000, A methodology for developing adaptive fuzzy cognitive maps for decision support, J. Adv. Comput. Intell., 4, 403 Kosko, 1986, Differential Hebbian learning, Neural Networks for Computing, American Institute of Physics, April, 277 Stach, 2005, Genetic learning of fuzzy cognitive maps, Fuzzy Sets Syst., 153, 371, 10.1016/j.fss.2005.01.009 Papageorgiou, 2004, Active Hebbian learning algorithm to train fuzzy cognitive maps, Int. J. Approx. Reason., 37, 219, 10.1016/j.ijar.2004.01.001 Mateou, 2008, A framework for developing intelligent decision support systems using evolutionary fuzzy cognitive maps, J. Intell. Fuzzy Syst., 19, 151 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). Froelich, 2009, Predictive capabilities of adaptive and evolutionary fuzzy cognitive maps—a comparative study, vol. 252, 153 Papakostas, 2008, Fuzzy cognitive maps for pattern recognition applications, Int. J. Pattern Recognition Artif. Intell., 22, 1461, 10.1142/S0218001408006910 Hoare, 2006, Pneumonia: update on diagnosis and management, Br. Med. J., 332, 1077, 10.1136/bmj.332.7549.1077 Langer, 1994, Diagnosis of bacterial infection in the ICU: general principles, Intensive Care Med., 20, 1232, 10.1007/BF01713977 Wikipedia, 〈http://en.wikipedia.org/wiki/pneumonia_severity_index〉. Fine, 1997, A prediction rule to identify low-risk patients with community-acquired pneumonia, N. Engl. J. Med., 336, 243, 10.1056/NEJM199701233360402 Pereira, 2004, Clinical signs of pneumonia in children: association with and prediction of diagnosis by fuzzy sets theory, Braz. J. Med. Biol. Res., 37, 701, 10.1590/S0100-879X2004000500012 Heckerling, 2004, Prediction of community-acquired pneumonia using artificial neural networks, Med. Decision Making, 23, 112, 10.1177/0272989X03251247 Visweswaran, 2005, Patient-specific models for predicting the outcomes of patients with community acquired pneumonia, 759 Heckerling, 2004, Use of genetic algorithms for neural networks to predict community-acquired pneumonia, Artif. Intell. Med., 30, 71, 10.1016/S0933-3657(03)00065-4 Papageorgiou, 2009, Fuzzy cognitive map based approach for assessing pulmonary infections, 109 Zadeh, 1965, Fuzzy sets, Inf. Control, 8, 338, 10.1016/S0019-9958(65)90241-X Dickerson, 1994, Virtual worlds as fuzzy cognitive map, Presence, 3, 173, 10.1162/pres.1994.3.2.173 Pelaez, 1995, Applying fuzzy cognitive maps knowledge representation to failure modes effects analysis, 450 Papageorgiou, 2010, Forecasting the state of pulmonary infection by the application of fuzzy cognitive maps, 1 A.V. Huerga, A balanced differential learning algorithm in fuzzy cognitive maps, in: Proceedings of the 16th International Workshop on Qualitative Reasoning, 2002. Papageorgiou, 2003, Fuzzy cognitive map learning based on nonlinear Hebbian rule, 256 Juszczuk, 2009, Learning fuzzy cognitive maps using a differential evolution algorithm, Pol. J. Environ. Stud., 12, 108 Stach, 2008, Numerical and linguistic prediction of time series with the use of fuzzy cognitive maps, IEEE Trans. Fuzzy Syst., 16, 61, 10.1109/TFUZZ.2007.902020 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. Bäck, 1997 Dietterich, 1998, Approximate statistical tests for comparing supervised classification learning algorithms, Neural Comput., 10, 1895, 10.1162/089976698300017197 Kohavi, 1995, A study of cross-validation and bootstrap for accuracy estimation and model selection, vol. 2, 1137 Krzanowski, 1997, Assessing error rate estimators: the leave-one-out method reconsidered, Aust. J. Stat., 39, 35, 10.1111/j.1467-842X.1997.tb00521.x