Background: Several models for mortality prediction have been constructed for critically ill patients with haematological malignancies in recent years. These models have proven to be equally or more accurate in predic...
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Background: Several models for mortality prediction have been constructed for critically ill patients with haematological malignancies in recent years. These models have proven to be equally or more accurate in predicting hospital mortality in patients with haematological malignancies than ICU severity of illness scores such as the APACHE II or SAPS II [1]. The objective of this study is to compare the accuracy of predicting hospital mortality in patients with haematological malignancies admitted to the ICU between models based on multiplelogisticregression (MLR) and support vector machine (SVM) based models. Methods: 352 patients with haematological malignancies admitted to the ICU between 1997 and 2006 for a life-threatening complication were included. 252 patient records were used for training of the models and 100 were used for validation. In a first model 12 input variables were included for comparison between MLR and SVM. In a second more complex model 17 input variables were used. MLR and SVM analysis were performed independently from each other. Discrimination was evaluated using the area under the receiver operating characteristic (ROC) curves (+/- SE). Results: The area under ROC curve for the MLR and SVM in the validation data set were 0.768 (+/- 0.04) vs. 0.802 (+/- 0.04) in the first model (p = 0.19) and 0.781 (+/- 0.05) vs. 0.808 (+/- 0.04) in the second more complex model (p = 0.44). SVM needed only 4 variables to make its prediction in both models, whereas MLR needed 7 and 8 variables in the first and second model respectively. Conclusion: The discriminative power of both the MLR and SVM models was good. No statistically significant differences were found in discriminative power between MLR and SVM for prediction of hospital mortality in critically ill patients with haematological malignancies.
Background: Knowledge is an important pre-requisite for implementing both primary as well as secondary preventive strategies for cardiovascular disease (CVD). There are no estimates of the level of knowledge of risk f...
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Objectives To test prospectively the diagnostic performance of two logisticregressionmodels for calculation of individual risk of malignancy in adnexal tumors (the 'Tailor model' and the 'Timmerman model...
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Objectives To test prospectively the diagnostic performance of two logisticregressionmodels for calculation of individual risk of malignancy in adnexal tumors (the 'Tailor model' and the 'Timmerman model'), and to compare them to that of 'pattern recognition' (subjective evaluation of the gray-scale ultrasound image and color Doppler ultrasound examination). Design Consecutive women with a pelvic mass judged clinically to be of adnexal origin underwent preoperative ultrasound examination including color and spectral Doppler examination. The same examination techniques and definitions as those used in the studies in which the logisticregressionmodels had been created were used. The Tailor model was tested in 133 women (35 of whom had a malignancy) and the Timmerman model in 82 women (29 of whom had a malignancy). A subset of 79 women (28 of whom bad a malignancy) was used to compare the performance of the Tailor model and the Timmerman model by calculating and comparing the areas under the receiver operating characteristics curves of the two models. Sensitivity and specificity with regard to malignancy were calculated for all three methods. Results Pattern recognition performed better than the two logisticregressionmodels (sensitivity around 85%, specificity around 90%). Using a risk of malignancy of > 50% to indicate malignancy (as suggested in the original publications), the sensitivity of the Tailor model was 69% and the specificity 88% (n = 133). The corresponding values for the Timmerman model were 62% and 79% (n = 82). The receiver operating characteristics curves showed the two logisticregressionmodels to have similar diagnostic properties (area under the curve, 0.87 vs. 0.84;P = 0.25;n = 79). The diagnostic performance of the mathematical models was much poorer in this study than in those in which the models had been created. Conclusion The poor diagnostic performance of the mathematical models can probably be explained by subtle differences in defini
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