Title :
Artificial neural networks: from black-box to grey-box modelling
Author :
Summers, R. ; Dybowski, R.
Author_Institution :
Dept. of Syst. Sci., City Univ., London, UK
Abstract :
The human decision-making process is simultaneously (and sometimes contradictory) logical, associative, fuzzy and probabilistic. To model this process faithfully, computer-aided medical diagnostic systems should possess all of these features. Therein lies one of the problems which hinders successful development of computer-based diagnostic aids: none of the software tools available has complete `coverage´ of the required features. One solution is to use more than one modelling paradigm to increase coverage. This paper describes a hybrid artificial neural network-causal probabilistic network modelling paradigm, developed to aid in the management of septicaemia. An emergent property is a method to visualise artificial neural networks in terms of the probability distributions of the causal probabilistic networks
Keywords :
blood; artificial neural network visualisation; causal probabilistic network; classification tool; computer-aided medical diagnostic systems; decision-making process; hybrid artificial neural network modelling paradigm; probability distributions; septicaemia management; software tools; Antibiotics; Artificial neural networks; Biology computing; Decision making; Decision support systems; Humans; Logistics; Medical treatment; Probability distribution; Statistical analysis;
Conference_Titel :
Engineering in Medicine and Biology Society, 1994. Engineering Advances: New Opportunities for Biomedical Engineers. Proceedings of the 16th Annual International Conference of the IEEE
Conference_Location :
Baltimore, MD
Print_ISBN :
0-7803-2050-6
DOI :
10.1109/IEMBS.1994.415324