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@Article{Kline:2005:AEM, author = "Jeffrey A. Kline and Andrew J. Novobilski and Christopher Kabrhel and Peter B. Richman and D. Mark Courtney", title = "Derivation and Validation of a {Bayesian} Network to Predict Pretest Probability of Venous Thromboembolism", journal = "Annals of Emergency Medicine", year = "2005", volume = "45", number = "3", pages = "282--290", month = mar, keywords = "genetic algorithms, genetic programming, Bayesian Networks, datamining", DOI = "doi:10.1016/j.annemergmed.2004.08.036", abstract = "Study objective A Bayesian network can estimate a numeric pretest probability of venous thromboembolism on the basis of values of clinical variables. We determine the accuracy with which a Bayesian network can identify patients with a low pretest probability of venous thromboembolism, defined as less than or equal to 2percent. Methods Using commercial software, we derived a population of Bayesian networks from 25 input variables collected on 3,145 emergency department (ED) patients with suspected venous thromboembolism who underwent standardised testing, including pulmonary vascular imaging, and 90-day follow-up (11.0percent of patients were venous thromboembolism positive). The best-fit Bayesian network was selected using a genetic algorithm. The selected Bayesian network was tested in a validation population of 1,423 ED patients prospectively evaluated for venous thromboembolism, including 90-day follow-up (8.0percent were venous thromboembolism positive). The Bayesian network probability estimate was normalised to a score of 0percent to 100percent. Results Of 1,423 patients in the validation cohort, 711 (50percent; 95percent confidence interval [CI] 47percent to 52percent) had a score less than or equal to 2percent that predicted a low pretest probability. Of these 711 patients, 700 (98.5percent; 95percent CI 97.2percent to 99.2percent) had no venous thromboembolism at follow-up. Conclusion A Bayesian network, derived and independently validated in ED populations, identified half of the validation cohort as having a low pretest probability (le 2percent); 98.5percent of these patients were correctly classified by the network.", notes = "SEE EDITORIAL, P. 291.", }

Genetic Programming entries for Jeffrey A Kline Andrew J Novobilski Christopher Kabrhel Peter B Richman D Mark Courtney