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Our approach, which is based on multivariate predictive models selects combination of genes to perform predict tumour features.
To propose multivariate predictive models for changes in pulmonary function tests (ΔPFTs) with respect to preradiotherapy (pre-RT) values in patients undergoing RT for breast cancer and lymphoma.
In this paper an energy-based Bayesian wavelet method is presented for validation assessment of multivariate predictive models under uncertainty, using time-series data collected from a dynamic system.
Objective: Our aim was to assess the utility and effectiveness of a neural network for predicting the likelihood of success of a trial of labor, relative to standard multivariate predictive models.
Brain morphometric data was employed to create multivariate predictive models using learning machine techniques (Figure 67).
Data from 197 patients admitted for coronary artery disease were examined using multivariate predictive models.
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The test characteristics of the multivariate predictive model and the neural network were compared.
A multivariate predictive model for mortality was constructed by using the variables that had significant interactions with the rate of demise (P ≤.1).
Finally, although our results suggest that the depth of thrombocytopenia was not an accurate predictor of severe bleeding, the retrospective design of the study and the quite low number of patients who bled precluded developing a more comprehensive and reliable multivariate predictive model of bleeding.
Lack of events hampered our ability to perform robust multivariate predictive modeling.
Slotman [ 12] described a multivariate predictive model imaginatively described as the SMART (Systemic Mediator Associated Response Test) system.
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