OBJECTIVE: 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. DESIGN: 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. MEASUREMENTS: 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. RESULTS: 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. CONCLUSION: 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.

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.

Examines an approach for integrating 3D structural reasoning, using computer models of the human anatomy, with diagnostic reasoning based on Bayesian networks in order to probabilistically predict injuries to anatomic structures from gunshot wounds. An interactive 3D graphical system has been created which allows the user to visualize different bullet path hypotheses and computes the probability that an anatomical structure associated with a given penetration path is injured. The probabilities derived are essential for mediating between structural reasoning and diagnostic reasoning.

The TraumAID system has been designed to provide on-line decision support throughout the initial definitive management of injured patients. Here we describe its retrospective evaluation and the use we subsequently made of judges comments on the validation data to evaluate TraumaTIQ, a new critiquing interface for TraumAID, investigating the question of whether, with timely recording of information, a system could produce commentary in line with that of human experts. Our results show that (1) comparable commentary can be produced, and (2) validation studies, which take great time and effort to conduct, can produce useful data beyond their original design goals.

Computing diagnoses in domains with continuously changing data is difficult but essential aspect of solving many problems. To address this task, a dynamic influence diagram (ID) construction and updating system (DYNASTY) and its application to constructing a decision-theoretic model to diagnose acute abdominal pain, which is a domain in which the findings evolve during the diagnostic process, are described. For a system that evolves over time, DYNASTY constructs a parsimonious ID and then dynamically updates the ID, rather than constructing a new network from scratch for every time interval. In addition, DYNASTY contains algorithms that test the sensitivity of the constructed network's system parameters. The main contributions are: (1) presenting an efficient temporal influence diagram technique based on parsimonious model construction; and (2) formalizing the principles underlying a diagnostic tool for acute abdominal pain that explicitly models time-varying findings.>

TraumAID is a consultation system for the diagnosis and treatment of multiple trauma. It integrates diagnostic reasoning, planning, and action. Its reasoner identifies diagnostic and therapeutic goals appropriate to the physician's knowledge of the patient's state, while its planner advises on beneficial actions to next perform. The physician's lack of complete knowledge of the situation and the time limitations of emergency medicine constrain the ability of any planner to identify what would be the best thing to do. TraumAID's Progressive Horizon Planner has been designed to create a plan for patient care that is in keeping with the standards of managing trauma.>