Computer methods and programs in biomedicine
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Comput Methods Programs Biomed · Oct 2008
Utility of multilayer perceptron neural network classifiers in the diagnosis of the obstructive sleep apnoea syndrome from nocturnal oximetry.
The aim of this study is to assess the ability of multilayer perceptron (MLP) neural networks as an assistant tool in the diagnosis of the obstructive sleep apnoea syndrome (OSAS). Non-linear features from nocturnal oxygen saturation (SaO(2)) recordings were used to discriminate between OSAS positive and negative patients. A total of 187 subjects suspected of suffering from OSAS (111 with a positive diagnosis of OSAS and 76 with a negative diagnosis of OSAS) took part in the study. ⋯ The selected MLP-based classifier provided a diagnostic accuracy of 85.5% (89.8% sensitivity and 79.4% specificity). Our neural network algorithm could represent a useful technique for OSAS detection. It could contribute to reduce the demand for polysomnographic studies in OSAS screening.
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Comput Methods Programs Biomed · Oct 2008
Improved attenuation correction via appropriate selection of respiratory-correlated PET data.
We propose a respiratory-correlated PET data processing method (called "BH-CT-based") based on breath-hold CT acquisition to reduce the smearing effect and improve the attenuation correction. The resulting images are compared with the ungated PET images acquired using a standard, free-breathing clinical protocol. ⋯ The application of a BH-CT-based method decreases motion bias in PET images. This method resolves issues related to both PET-to-CT misregistration and erroneous attenuation correction and increases lesion detectability.
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Comput Methods Programs Biomed · Oct 2008
A set of SAS macros for calculating and displaying adjusted odds ratios (with confidence intervals) for continuous covariates in logistic B-spline regression models.
In clinical and epidemiologic research to investigate dose-response associations, non-parametric spline regression has long been proposed as a powerful alternative to conventional parametric regression approaches, since no underlying assumptions of linearity have to be fulfilled. For logistic spline models, however, to date, little standard statistical software is available to estimate any measure of risk, typically of interest when quantifying the effects of one or more continuous explanatory variable(s) on a binary disease outcome. ⋯ The macros are easily to use and can be implemented quickly by the clinical or epidemiological researcher to flexibly investigate any dose-response association of continuous exposures with the risk of binary disease outcomes. We illustrate the application of our SAS codes by investigating the effect of body-mass index on risk of cancer incidence in a large, population-based male cohort.