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Referencias:

• Torrington, K.G., Henderson, C.J., Perioperative respiratory therapy (PORT). A program of perioperative risk assessment and individualized postoperative care (1988) Chest, 95, pp. 947-951
• Goldman, L., Caldera, D.L., Nussbaum, M., Multifactorial index of cardiac risk in noncardiac surgical procedures (1977) N Engl J Med, 297, pp. 845-850
• Esteva, H., Luna, C.M., Loterzo, A., Predictive capacity of risk indices in pulmonary resections (1995) South Am J Thoracic Surg, 1, pp. 11-14
• Vollmer, R.T., Multivariate statistical analysis for pathologists. Part I: the logistic model (1996) Am J Clin Pathol, 105, pp. 115-126
• Bellotti, M., Elsner, B., Paes De Lima, A., Neural networks as a prognostic tool for patients with non-small cell carcinoma of the lung (1997) Mod Pathol, 10, pp. 1221-1227
• Clancey, W.J., Shortliffe, E.H., (1984) Readings in medical artificial intelligence-The first decade, , Addison- Wesley, Reading (MA)
• Mc Culloch, W.S., Pitts, W., A logical calculus of the ideas imminent in nervous activity (1943) Bull Math Biophys, 5, pp. 115-133
• Coiera, E., (2003) Guide to health informatics. 2nd edition. part 8 capt 25, , Hodder Arnold Publication, Sydney
• An, C.S., Petrovic, L.M., Reyter, I., The application of image analysis and neural network technology to the study of large-cell liver-cell dysplasia and hepatocellular carcinoma (1997) Hepatology, 26, pp. 1224-1231
• Marchevsky, A.M., Patel, S., Wiley, K.J., Reasoning with uncertainty in Pathology: artificial neural networks and logistic regression as tools for prediction of survival in Stage I and II non-small cell lung cancer patients (1998) Mod Pathol, 11, pp. 618-625
• Mango, L.J., Computer-assisted cervical cancer screening using neural networks (1994) Cancer Lett, 77, pp. 155-162
• Rosenthal, D.L., Acosta, D., Peters, R.K., Computer-assisted rescreening of clinically important false negative cervical smears using the PAPNET Testing System (1996) Acta Cytol, 40, pp. 120-126
• Xu, X.W., Doi, K., Kobayashi, T., Development of an improved CAD scheme for automated detection of lung nodules in digital chest images (1997) Med Phys, 24, pp. 1395-1403
• Fogel, D.B., Wasson, E.C., Boughton, E.M., Evolving artificial neural networks for screening features from mammograms (1998) Artif Intell Med, 14, pp. 317-326
• Turton, E.P., Scott, D.J., Delbridge, M., Ruptured abdominal aortic aneurysm: a novel method of outcome prediction using neural network technology (2000) Eur J Vasc Endovasc Surg, 19, pp. 184-189
• Segal, M.E., Goodman, P.H., Goldstein, R., The accuracy of artificial neural networks in predicting long-term outcome after traumatic brain injury (2006) J Head Trauma Rehabil, 21, pp. 298-314
• Arle, J.E., Perrine, K., Devinsky, O., Neural network analysis of preoperative variables and outcome in epilepsy surgery (1999) J Neurosurg, 90, pp. 998-1004
• Parmanto, B., Doyle, H.R., Recurrent neural networks for predicting outcomes after liver transplantation: representing temporal sequence of clinical observations (2001) Methods Inf Med, 40, pp. 386-391
• Lippmann, R.P., Shahian, D.M., Coronary artery by-pass risk prediction using neural networks (1997) Ann Thorac Surg, 63, pp. 1635-1643
• Fayad, U., Piatetsky-Shapiro, G., Smyth, P., "From data mining to knowledge discovery in databases" (1996) AI Magazine, 17, pp. 37-54
• Lavrac, N., Data mining in medicine: selected techniques and applications (1999) Artif Intell Med, 16, pp. 3-23
• Werbos, P., (1974) Beyond regression: new tools for prediction and analysis in the behavioral sciences, , Harvard University Department of Applied Mathematics, Cambridge (MA)
• Kohonen, T., (1997) Associative memory: a system theoretic approach, , Springer, New York
• Hopefield, J.J., Neural networks and physical systems with emerging collective computational abilities (1982) Proc Natl Acad Sci U S A, 81, pp. 3088-3092
• Drew, P.J., Monson, J.R., Artificial neural networks (2000) Surgery, 127, pp. 3-11
• Cross, S.S., Harrison, R.F., Kennedy, R.L., Introduction to neural networks (1995) Lancet, 346, pp. 1075-1079
• Esteva, H., Bellotti, M., Marchevsky, A.M., Neural Networks for the estimation of prognosis in lung cancer (2001) Artificial Neural Networks in cancer diagnosis, prognosis and patient management, p. 29. , Naguib R.N.G., and Sherbet G.V. (Eds), CRC Press LLC, Boca Raton
• Fletcher, J.M., Rice, W.J., Ray, R.M., Linear discriminant function analysis in neuropsychological research: Some uses and abuses (1978) Cortex, 14, pp. 564-565
• Pass, H.I., Liu, Z., Wali, A., Gene expression profiles predict survival and progression of pleural mesothelioma (2004) Clin Cancer Res, 10, pp. 849-859
• Poulakis, V., Witzsch, U., de Vries, R., Preoperative neural network using combined magnetic resonance imaging variables, prostate-specific antigen, and Gleason score for predicting prostate cancer biochemical recurrence after radical prostatectomy (2004) Urology, 64, pp. 1165-1170
• Muers, M.F., Shevlin, P., Brown, J., Prognosis in lung cancer: physicians' opinion compared with outcome and a predictive model (1996) Thorax, 51, pp. 894-902
• Batuello, J.T., Gamito, E.J., Crawford, E.D., Artificial neural network model for the assessment of lymph node spread in patients with clinically localized prostate cancer (2001) Urology, 57, pp. 481-485
• Chong, C.F., Li, Y.C., Wang, T.L., Stratification of adverse outcomes by preoperative risk factors in coronary artery bypass graft patients: an artificial neural network prediction model (2003) AMIA Annu Symp Proc, pp. 160-164
• Buzatu, D.A., Taylor, K.K., Peret, D.C., The determination of cardiac surgical risk using artificial neural networks (2001) J Surg Res, 95, pp. 61-66
• Lapuerta, P., L'Italien, G.J., Paul, S., Neural network assessment of perioperative cardiac risk in vascular surgery patients (1998) Med Decis Making, 18, pp. 70-75
• Nilsson, J., Ohlsson, M., Thulin, L., Risk factors identification and mortality prediction in cardiac surgery using artificial neural networks (2006) J Thorac Cardiovasc Surg, 132, pp. 12-19
• Santos García, G., Varela, G., Novoa, N., Prediction of postoperative morbidity after lung resection using an artificial neural network ensemble (2004) Artif Intell Med, 30, pp. 61-69
• Esteva, H., Marchevsky, A., Nuñez, T., Neural networks as a prognostic tool of surgical risk in lung resections (2002) Ann Thorac Surg, 73, pp. 1576-1581
• (2003) Prevención y Manejo de las Complicaciones de la Cirugía Torácica, , Esteva y colaboradores, Editorial EDUCA, Buenos Aires
• Breinman, L., (1994) Bagging predictors, , Dpt of Statistics, University of California, Berkeley;

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