One-year mortality prognosis in XXXX : a neural network approach based on echocardiographic data.

@entity1 @entity712 @entity1274 @entity296
OBJECTIVES: This study sought to assess the usefulness and accuracy of artificial @entity1274 in the prognosis of 1-year mortality in @entity1 with @entity712 . BACKGROUND: Artificial @entity1274 is a computational technique used to represent and process information by means of networks of interconnected processing elements, similar to neurons. They have found applications in medical decision support systems, particularly in prognosis. METHODS: Clinical and Doppler-derived echocardiographic data from 95 consecutive @entity1 with diffuse impairment of @entity296 were studied. After 1 year, data regarding survival or death were obtained and produced the prognostic variable. The data base was divided randomly into a training data set (47 cases, 8 deaths) and a testing data set (48 cases, 7 deaths). Results of artificial neural network classification were compared with those from linear discriminant analysis, clinical judgment and conventional heuristically based programs. RESULTS: The study group included 57 male (47 survivors) and 38 female @entity1 (33 survivors). Linear discriminant analysis was not efficient for separating survivors from nonsurvivors because the accuracy at the ideal cutoff value was only 67.4%, with a sensitivity of 67.5%, positive predictive value of 27.8% and negative predictive value of 91.5%. In contrast, all artificial @entity1274 were able to predict outcome with an accuracy of 90%, specificity of 93% and sensitivity of 71.4%, for the best artificial neural network. Both clinical judgment and automatic heuristic methods were also inferior in performance. CONCLUSIONS: The artificial neural network method has proved to be reliable for implementing quantitative prognosis of mortality in @entity1 with @entity712 . Additional studies with larger numbers of @entity1 are required to better assess the usefulness of artificial @entity1274 .
