Abductive localization of brain damage: incorporating spatial adjacency relations

Authors:
Stanley Tuhrim;Deborah R. Horowitz;James A. Reggia;Sharon M. Goodall
Affiliations:
Department of Neurology, Mount Sinai School of Medicine, One Gustave L. Levy Place, New York, NY 10029, USA;Department of Neurology, Mount Sinai School of Medicine, One Gustave L. Levy Place, New York, NY 10029, USA;Department of Computer Science, A. V. Williams Building, University of Maryland, College Park, MD 20742, USA;Department of Computer Science, A. V. Williams Building, University of Maryland, College Park, MD 20742, USA
Venue:
Artificial Intelligence in Medicine
Year:
1991

Citing 2
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A probabilistic causal model for diagnostic problem solving, part one: integrating symbolic causal inference with numeric probabilistic inference

IEEE Transactions on Systems, Man and Cybernetics - Special issue on artificial intelligence
Abductive inference models for diagnostic problem-solving

Abductive inference models for diagnostic problem-solving

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Abstract

An important problem in neurology is to localize the site of damage to the nervous system given a patient's examination findings. While the reasoning processes involved in this neurologic localization task are a type of diagnostic reasoning, they are distinguished by their heavy use of anatomical (spatial) relationships. Previous attempts to automate neurological localization have met with limited success. This paper describes an abductive problem-solving method for neurological localization based on parsimonious covering theory (PCT). Basic PCT is augmented by adding spatial relationships between elementary anatomic units. Our model's localization for 100 stroke patients was compared to that of a neurologist specializing in stroke who was not involved with the model's development. In 99 cases, the problem-solving system based on the augmented PCT algorithm identified the location of nervous system damage (brainstem or either hemisphere) found by the stroke expert. In the one case of complete disagreement, the problem-solving system was proven correct. Examination of the detailed localizations in terms of the elementary anatomical units involved indicated a number of interesting differences between human and automated inference processes. These results demonstrate that an augmented PCT approach has substantial promise for neurological localization.