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

0 Bookmarks
 · 
49 Views
  • [Show abstract] [Hide abstract]
    ABSTRACT: The heterogeneity of Schizophrenia disease has been a major pitfall for identifying the aetiological, genetic or environmental factors. Age at onset or several other quantitative variables could allow for categorizing more homogeneous subgroups of patients, although there is little information on which are the boundaries for such categories. The Bayesian networks classifier approach is one of the most popular formalisms for reasoning under uncertainty. We used this approach to determine the best cut-off point for three continuous variables (i.e. age at onset of schizophrenia (AFC* & AFE**) and neurological soft signs (NSS)) with a minimal loss of information, using a data set including genotypes of selected candidate genes for schizophrenia.
    Cognitive Informatics, 2005. (ICCI 2005). Fourth IEEE Conference on; 09/2005
  • [Show abstract] [Hide abstract]
    ABSTRACT: We show that Bayesian methods can be efficiently applied to the classification of otoneurological diseases and to assess attribute dependencies. A set of 38 otoneurological attributes was employed in order to use a naive Bayesian probabilistic model and Bayesian networks with different scoring functions for the classification of cases from six otoneurological diseases. Tests were executed on the basis of tenfold crossvalidation. We obtained average sensitivities of 90%, positive predictive values of 92% and accuracies as high as 97%, which is better than our earlier tests with neural networks. Our assessments indicated that Bayesian methods have good power and potential to classify otoneurological patient cases correctly even if this is often a complicated task for the best specialists. Bayesian methods classified the current medical data and knowledge well.
    Journal of Medical Systems 04/2010; 34(2):119-30. · 1.78 Impact Factor
  • [Show abstract] [Hide abstract]
    ABSTRACT: Contracting for availability and contracting for capability are becoming increasingly common practices in the defence world. With these new service-oriented contracts, the responsibility for through-life support, including maintenance, has been shifted from the user to the service provider. In this new environment, innovative approaches to improving maintenance and reliability are necessary and create new, unique opportunities for value co-creation between stakeholders. This chapter focuses on investigating the applicability and implementation of an approach to predictive maintenance which combines prognostic modelling with Condition Based Maintenance (CBM) and its role in providing improved service provision for the repair and maintenance of complex systems. The role of prognostic modelling and Health and Usage Monitoring Systems as the emerging technologies that enable a value-oriented approach to maintenance are discussed. Bayesian networks are discussed as a modelling framework that is appropriate to capture uncertainties related to predictive maintenance. Special focus is placed on reviewing practical challenges and proposing solutions to them. The discussion is summarised in the form of a practitioner’s guide to implementing prognostic modelling and CBM.
    12/2010: pages 277-296;

Full-text (2 Sources)

View
29 Downloads
Available from
May 23, 2014