Conference Paper

Using Bayesian Networks for Diagnostic Reasoning in Penetrating Injury Assessment.

Decision Syst. Group, Brigham & Women's Hospital, Boston, MA
DOI: 10.1109/CBMS.2000.856888 Conference: 13th IEEE Symposium on Computer-Based Medical Systems (CBMS 2000), 23-24 June 2000, Houston, TX, USA
Source: DBLP

ABSTRACT

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 injuries arises from two different sources: the actual extent of damage associated with a particular injury mechanism 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 the injury. Bayesian networks are used in TraumaSCAN for diagnostic reasoning because they provide a mathematically sound means of making probabilistic inferences about the 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

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    • "The system is essentially a Bayesian network modelling the causal relationships among variables as elicited from relevant literature concentrating on the caring procedure appropriate for such patients, as well as direct questioning of domain experts via questionnaires . Similar work has been carried out in the probabilistic reformulation of the INTERNIST-1/QMR [11], the HEPAR [12], and the Trauma SCAN [13] systems. However the knowledge representation and the inference techniques used for the DIMITRA-Pro system are considerably different to those used in the above systems. "
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    ABSTRACT: This paper describes a probabilistic causal model for the caring procedure to be followed on wheelchair users with spinal injury. Due to loss of sensation and movement caused by spinal cord injuries, the information extracted about patient findings (i.e. the signs and symptoms) can often be incomplete. This, in turn, introduces uncertainty in assessing the existence and severity of a given condition-and thus, employment of the appropriate caring procedure. Bayesian networks are a framework that enables probabilistic inference; therefore, they are useful for diagnostic reasoning and selection of the appropriate caring procedure in the face of uncertainty. The network structure and numerical parameters are based on data elicited from the qualified staff nurses and available literature of the National Spinal Injury Centre, Stoke Mandeville Hospital, Aylesbury, UK, as well as the compiled knowledge base within the DIMITRA rule-based expert system [M. Athanasiou, J.Y. Clark, DIMITRA: an online expert system for carers of paraplegics and quadriplegics, International Journal of Healthcare Technology and Management 7(5) (2006) 44-451]. We also present the model and report the results of the diagnostic performance tests using the AgenaRiskn [Agena Limited, AgenaRisk Software Package, http://www.agena.co.uk] Bayesian network package.
    Preview · Article · May 2009 · Computer methods and programs in biomedicine
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    • "The system is essentially a Bayesian network modelling the causal relationships among variables as elicited from relevant literature concentrating on the caring procedure appropriate for such patients, as well as direct questioning of domain experts via questionnaires . Similar work has been carried out in the probabilistic reformulation of the INTERNIST-1/QMR [11], the HEPAR [12], and the Trauma SCAN [13] systems. However the knowledge representation and the inference techniques used for the DIMITRA-Pro system are considerably different to those used in the above systems. "
    [Show abstract] [Hide abstract]
    ABSTRACT: This paper describes a probabilistic causal model for the caring procedure to be followed on wheelchair users with spinal injury. Uncertainty in the caring procedure arises mostly from incomplete information about patient findings (i.e. the signs and symptoms) due to loss of sensation and movement caused by the spinal cord injury. As a result, it may not be easy to assess the extent of a condition -- and, thus, make an accurate diagnosis. Bayesian networks are used for diagnostic reasoning because they offer a way of conducting probabilistic inference about the conditions associated with the caring procedure in the face of uncertainty. The network structure and numerical parameters are based on data elicited from the qualified staff nurses and literature of the National Spinal Injury Centre, Stoke Mandeville Hospital, Aylesbury, UK. We also present the model and report the results of the diagnostic performance tests using the AgenaRisk Bayesian network package.
    Preview · Conference Paper · Jul 2007
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