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Combining geometric and probabilistic reasoning for computer-based penetrating-trauma assessment
Journal article   Open access

Combining geometric and probabilistic reasoning for computer-based penetrating-trauma assessment

Omolola I Ogunyemi, John R Clarke, Nachman Ash and Bonnie L Webber
Journal of the American Medical Informatics Association : JAMIA, v 9(3)
May 2002
DOI: https://doi.org/10.1197/jamia.M0979
PMID: 11971888
Featured in Collection :   UN Sustainable Development Goals @ Drexel
url
https://doi.org/10.1197/jamia.m0979View
Published, Version of Record (VoR)Maybe Open Access (Publisher Bronze),  Open
url
https://doi.org/10.1197/jamia.M0979View
Published, Version of Record (VoR) Open

Abstract

Bayes Theorem Computer Simulation Diagnosis, Computer-Assisted Humans Image Processing, Computer-Assisted Mathematical Computing Models, Anatomic Neural Networks (Computer) Probability Retrospective Studies ROC Curve User-Computer Interface Wounds, Gunshot - diagnosis
To ascertain whether three-dimensional geometric and probabilistic reasoning methods can be successfully combined for computer-based assessment of conditions arising from ballistic penetrating trauma to the chest and abdomen. The authors created a computer system (TraumaSCAN) that integrates three-dimensional geometric reasoning about anatomic likelihood of injury with probabilistic reasoning about injury consequences using Bayesian networks. Preliminary evaluation of TraumaSCAN was performed via a retrospective study testing performance of the system on data from 26 cases of actual gunshot wounds. Areas under the receiver operating characteristics (ROC) curve were calculated for each condition modeled in TraumaSCAN that was present in the 26 cases. The comprehensiveness and relevance of the TraumaSCAN diagnosis for the 26 cases were used to assess the overall performance of the system. To test the ability of TraumaSCAN to handle limited findings, these measurements were calculated both with and without input of observed findings into the Bayesian network. For the 11 conditions assessed, the worst area under the ROC curve with no observed findings input into the Bayesian network was 0.542 (95% CI, 0.146-0.937), the median was 0.883 (95% CI, 0.713-1.000), and the best was 1.00 (95% CI, 1.000-1.000). The worst area under the ROC curve with all observed findings input into the Bayesian network was 0.835 (95% CI, 0.602-1.000), the median was 0.941 (95% CI, 0.827-1.000), and the best was 0.992 (95% CI, 0.965-1.000). A comparison of the areas under the curve obtained with and without input of observed findings into the Bayesian network showed that there were significant differences for 2 of the 11 conditions assessed. A computer-based method that combines geometric and probabilistic reasoning shows promise as a tool for assessing ballistic penetrating trauma to the chest and abdomen.

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11 Record Views
15 citations in Web of Science
17 citations in Scopus

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UN Sustainable Development Goals (SDGs)

This publication has contributed to the advancement of the following goals:

#3 Good Health and Well-Being

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Collaboration types
Domestic collaboration
International collaboration
Web of Science research areas
Computer Science, Information Systems
Computer Science, Interdisciplinary Applications
Health Care Sciences & Services
Information Science & Library Science
Medical Informatics
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