By Richard Dybowski, Vanya Gant
Synthetic neural networks presents a robust software to aid medical professionals study, version, and make experience of complicated medical info throughout a vast diversity of clinical functions. Their capability in medical medication is mirrored within the variety of issues lined during this state of the art quantity. as well as taking a look at new and approaching functions the ebook seems ahead to intriguing destiny customers at the horizon. the quantity additionally examines moral and criminal matters in regards to the use of "black-box" platforms as choice aids in medication. This eclectic choice of chapters presents an exhilarating evaluation of present and destiny customers for harnessing the facility of man-made neural networks within the research and remedy of disorder.
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Additional info for Clinical Applications of Artificial Neural Networks
Gant, V. (1996). Prediction of outcome in critically ill patients using artiWcial neural network synthesised by genetic algorithm. Lancet 347, 1146– 1150. Elman, J. L. (1990). Finding structure in time. Cognitive Science 14, 179–211. Fahlman, S. E. (1988). Faster-learning variations on back-propagation: an empirical study. In D. Touretzky, G. Hinton & T. , Proceedings of the 1988 Connectionist Models Summer School. Morgan Kaufmann, San Mateo, CA, pp. 38–51. Friedman, J. H. (1991). Multivariate adaptive regression splines (with discussion).
Cross The hybrid classifier system with neural network processing and statistical classifiers used by Stotzka et al. (1995) to grade prostatic carcinoma. spectra. Chun et al. used multilayer perceptrons, trained by the back-propagation method, to identify novel species of Streptomyces and to discriminate between these and unknown organisms (Chun et al. 1993). They used a network with 150 input neurons entering scaled and normalized integrated ion counts at unit mass intervals from 51 to 200. The network contained a hidden layer of 10 neurons and, in its Wnal version, four output neurons.
Broomhead, D. S. & Lowe, D. (1988). Multivariable functional interpolation and adaptive networks. Complex Systems 2, 321–355. Buntine, W. L. (1992). Learning classiWcation trees. Statistics and Computing 2, 63–73. Buntine, W. L. (1994). Operations for learning with graphical models. Journal of ArtiWcial Intelligence Research 2, 159–225. Buntine, W. L. & Weigend, A. S. (1991). Bayesian back-propagation. Complex Systems 5, 603–643. Carpenter, G. A. & Grossberg, S. (1987). A massively parallel architecture for a self-organizing neural pattern recognition machine.