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Tabares O., H. A. (2012). Mapeo de la evolución de una enfermedad usando sistemas Neuro-Difusos. Caso de estudio: Esclerosis Múltiple. Ingenierías USBmed, 3(1), 69–73. https://doi.org/10.21500/20275846.266
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Abstract
El presente artículo propone un modelo para estudiar la evolución de la esclerosis múltiple, enfermedad desmielinizante, neurodegenerativa y crónica del sistema nervioso central. El modelo planteado se basa en la utilización de un sistema neuro-difuso como herramienta para describir la progresión de la enfermedad, empleando un caso particular para su validación. Los datos de estudio corresponden a la historia clínica de un paciente con diagnóstico de esclerosis múltiple desde 2003, quien ha padecido cinco episodios críticos. El modelo desarrollado permitió detectar los cambios de la inflación neurológica del paciente.
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L. Ohno. “Modular neural networks for medical prognosis: quantifying the benefits of combining neural networks for survival prediction”. Connection Science: Journal of Neural Computing, Artificial Intelligence and Cognitive Research, Vol. 9, No. 1, pp.71-86, 1997.
B. Martin del Brio & A. Sanz. “Redes Neuronales y Sistemas Difusos”. Alfa-Omega, 2002.
Y. Chiou & Y. Lure. “Hybrid lung module detection (HLND) system”. Cancer Letters, Vol. 77, No. 2-3, pp. 119-126, 1994.
C. Sthepan et al. “Three new serum markers for prostate cancer detection within a percent free PSA-based artificial neural network”. Prostate, Vol. 66, No. 6. pp. 651-657, 2006.
Y. Wang & J. Shiun. “Artificial Neural Network to Predict Skeletal Metastasis in Patients with Prostate Cancer”. Medical Systems, Vol. 33, No. 2, pp. 91-100, 2009.
A. Celona; G. Grasso & L. Puccio. “Artificial Neural Network (ANN) Morphological Classification by Euclidean Distance Histograms for Prognostic Evaluation of Magnetic Resonance Imaging in Multiple Sclerosis”. Proceedings SIMAI Congress, Vol. 3, pp. 283-292, 2009.
F. Buarque. “Multiple Sclerosis Plaque: Computer Model and Simulations”. Internal Report, Imperial College of Science Technology and Medicine, 2000.
D. Bizios; A. Heij & B. Bengtsson. “Trained artificial neural network for glaucoma diagnosis using visual field data: A comparison with conventional algorithms”. Journal of Glaucoma, Vol. 16, No. 1, pp. 20-28, 2007.
M. Astion et al. “Application of neural networks to the classification of giant cell arteritis”. Arthritis and Rheumatism, Vol. 37, No. 5, pp.760-70, 1994.
K. Borges; R. Moura & A. Steiner. “Diagnosis of Headache using Artificial Neural Networks”. International Journal of Computer Science and Network Security, Vol. 10, No.7, pp. 172-178, 2010.
V. Bourdes et al. “Breast cancer predictions by neural networks analysis: a comparison with logistic regression”. Proceedings 29th Annual International Conference Medicine and Biology Society, pp. 5424-5427, 2007.
D. Cook. “Artificial Neural Networks to Predict Mortality in Critical Care Patients: An Application of Supervised Machine Learning”. Australasian Anesthesia, Vol. 122, No 2, pp. 173-179, 2005.
H. Doyle et al. “Predicting outcomes after liver transplantation. A connectionist approach”. Annals of Surgery, Vol. 219, No. 4, pp. 408-415, 1994.
D. Gil et al. “Application of artificial neural networks in the diagnosis of urological dysfunctions”. Expert Systems with Applications, Vol. 36, No. 3, pp. 5754-5760, 2009.
M. Ebell. “Artificial neural networks for predicting failure to survive following in-hospital cardiopulmonary resuscitation”. Journal of Family Practice, Vol. 36, No. 3, pp. 297-303, 1993.
R. Harrison & L. Kennedy. “Artificial Neural Network Models for Prediction of Acute Coronary Syndromes Using Clinical Data from the Time of Presentation”. Annals of Emergency Medicine, Vol. 46, No. 5, pp. 431-440, 2005.
C. Kazmierczak; G. Catrou & F. Van Lente. “Diagnostic accuracy of pancreatic enzymes evaluated by use of multivariate data analysis”. Clinical Chemistry, Vol. 39, No. 9, pp. 1960-1965, 1993.
A. Bartosch; B. Andersson & J. Nilsson. “Artificial neural networks in pancreatic disease”. British Journal of Surgery, Vol. 95, No. 7, pp. 817-826, 2008.
P. Sharpe et al. “Artificial neural networks in diagnosis of thyroid function from in vitro laboratory tests”. Clinical Chemistry, Vol. 39, No. 11, pp. 2248-2253, 1993.
A. Gannous & R. Elhaddad. “Improving an Artificial Neural Network Model to Predict Thyroid Bending Protein Diagnosis Using Preprocessing Techniques”. World Academy of Science, Engineering and Technology, Vol. 74, pp. 126-130, 2011.
M. Moncada & H. Cadavid. “Estimación de variables eléctricas en un muslo 3D con fractura de diáfisis femoral estimulado magnéticamente”. Revista Facultad de Ingeniería Universidad de Antioquia, No. 42, pp. 120-131, 2007.
M. Moncada et al. “Desarrollo experimental y computacional para estimar variables eléctricas inducidas en muestras de fémur bovino estimuladas por campos magnéticos de baja frecuencia”. Revista Cubana de Investigaciones Biomédicas, Vol. 27, No. 2, pp. 1-9, 2008
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