In addition, our defined Δ p ^ illustrated the importance of including a comorbidity index in a model, as opposed to using the base model, in predicting the 1-year mortality for patients 50 years of age or older.
Our model had significantly higher accuracy than the base model in predicting cancer (area-under-the-curve [AUC] improved from 0.63 to 0.78) or high-grade cancer (AUC increased from 0.77 to 0.87).
The comparison of the new approach with existing empirical and experimental data shows good precision and accuracy of the developed ANN-based model in predicting the FRP-confined compressive strength of concrete.
The comparison of the proposed prognostic approach with existing empirical and experimental data exhibits a very good precision of the developed artificial intelligence-based model in predicting the lateral confinement coefficient in CFRP wrapped RC columns.
Previous studies showed that infinitesimal GP model (GBLUP or ridge regression models) outperformed QTL-based model in predicting complex traits for both multifamily populations (Peiffer et al. 2014) and biparental segregating populations (Lorenzana and Bernardo 2009; Guo et al. 2012).
However, the UCI model performed worse than the rule-based model in predicting HCTLS data, likely because of the very small and non-representative training dataset for the development of the UCI model.
The aim of this study is to develop a comprehensive CFD based model for predicting hydrodynamic parameters in trickle-bed reactors under cold-flow conditions.
With other scoring systems frequently employed in critical care areas, research describing the appropriateness of POSSUM based models to predict mortality in areas delivering higher dependency care is sparse.
