High-specificity neurological localization using a connectionist model.
Artif Intell Med
; 6(6): 521-32, 1994 Dec.
Article
en En
| MEDLINE
| ID: mdl-7858663
ABSTRACT
Most previous connectionist models for diagnosis have been developed using error backpropagation. While these systems function reasonably well, they have been limited by their need for a large database of test cases, to situations where a single disorder is present, and by the large number of connections required between fully-connected sets of processing units. Here we describe a recently developed connectionist model that overcomes these limitations. This approach can reuse existing causal knowledge bases, works well in situations where multiple disorders can occur simultaneously, and does not require fully-connected sets of processing units. We demonstrate that the accuracy of this model is comparable to that of more conventional AI programs using the same knowledge base in determining precisely the site of brain damage in a group of 50 stroke patients. These results support the conclusion that connectionist models can effectively use pre-existing causal knowledge bases from AI systems, and that they can function accurately when handling actual clinical problems.
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Banco de datos:
MEDLINE
Asunto principal:
Inteligencia Artificial
/
Trastornos Cerebrovasculares
/
Redes Neurales de la Computación
/
Modelos Neurológicos
Tipo de estudio:
Diagnostic_studies
/
Prognostic_studies
Límite:
Humans
Idioma:
En
Revista:
Artif Intell Med
Asunto de la revista:
INFORMATICA MEDICA
Año:
1994
Tipo del documento:
Article