Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in expert systems: theory and algorithms
Probabilistic reasoning in expert systems: theory and algorithms
Probabilistically Predicting Penetrating Injury for Decision Support
CBMS '98 Proceedings of the Eleventh IEEE Symposium on Computer-Based Medical Systems
Traumascan: assessing penetrating injury with abductive and geometric reasoning
Traumascan: assessing penetrating injury with abductive and geometric reasoning
MUNIN: a causal probabilistic network for interpretation of electromyographic findings
IJCAI'87 Proceedings of the 10th international joint conference on Artificial intelligence - Volume 1
Computer Methods and Programs in Biomedicine
Classification of Otoneurological Cases According to Bayesian Probabilistic Models
Journal of Medical Systems
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This paper describes a method for diagnostic reasoning under uncertainty that is used in TraumaSCAN, a computer-based system for assessing penetrating trauma. Uncertainty in assessing penetrating injury arises from different sources: the actual extent of damage associated with a particular mechanism of injury may not be easily discernable, and there may be incomplete information about patient findings (signs, symptoms, and test results), which provide clues about the extent of injury. Bayesian networks are used in TraumaSCAN for diagnostic reasoning because they provide a mathematically sound means of making probabilistic inferences about injury in the face of uncertainty. We also present a comparison of TraumaSCAN's results in assessing 26 actual gunshot wound cases with those of TraumAID, a validated rule-based expert system for the diagnosis and treatment of penetrating trauma.