Pain relief with vertebroplasty is similar in patients with and those without intravertebral fracture clefts. Because of the small number of unfilled fracture clefts in our population, the true incidence of post-percutaneous vertebroplasty pain in patients with an un-filled cleft remains uncertain.

Objective-To classify patients by age based upon information extracted from their electrocardiograms (ECGs). To develop and compare the performance of Bayesian classifiers.Methods and Material-We present a methodology for classifying patients according to statistical features extracted from their ECG signals using a genetically evolved Bayesian network classifier. Continuous signal feature variables are converted to a discrete symbolic form by thresholding, to lower the dimensionality of the signal. This simplifies calculation of conditional probability tables for the classifier, and makes the tables smaller. Two methods of network discovery from data were developed and compared: the first using a greedy hill-climb search and the second employed evolutionary computing using a genetic algorithm (GA).Results and Conclusions-The evolved Bayesian network performed better (86.25% AUC) than both the one developed using the greedy algorithm (65% AUC) and the naïve Bayesian classifier (84.75% AUC). The methodology for evolving the Bayesian classifier can be used to evolve Bayesian networks in general thereby identifying the dependencies among the variables of interest. Those dependencies are assumed to be non-existent by naïve Bayesian classifiers. Such a classifier can then be used for medical applications for diagnosis and prediction purposes.

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Atrial fibrillation (AF) and flutter are common following cardiac surgery, increasing costs and morbidity. Cardiologists need a method to discern those patients who are at high risk for this arrhythmia in order to attempt to treat them by either pharmacologic or non-pharmacologic means. We performed a retrospective analysis of 377 CABG patients, of which 94 developed AF post-operatively. Feature selection and AF occurrence prediction was performed using a multivariate regression model, and two rough set derived rule classifiers. The rough set derived feature subset performed best with an accuracy of 87%, a sensitivity of 58.5%, and a specificity of 96.5%. This shows the importance of testing feature subsets, thereby discouraging the practice of simply combining the best individual predictors. The utility of rough set theory in prediction of cardiac arrhythmia is also validated.