Since chorioamnionitis is clinically silent in most instances, and histologic analysis of the placenta takes considerably long, a prediction model based on clinical parameters which are available upon delivery might support clinical decision-making [88].
Possible prognostic factors from demographic, clinical and medical variables will be selected for a multivariable model based on clinical and statistical significance.
Samani (2005) designed FPC Model based on clinical observation, family interview and experimental data.
In this paper, we investigate whether the predictive power of a survival model based on clinical patient features improves when MRI features are also included in the model.
I will present a computational model based on clinical and psychophysical data that accurately predicts these distortions across a wide range of subjects and implant configurations.
We assessed the impact of excluding patients who did not undergo chest radiography by building a model based on clinical variables only.
Variables were included in the model based on clinical relevance.
We first selected potentially useful baseline characteristic predictor variables for the multivariable model based on clinical experience and previous literature.
A predictive model based on clinical characteristics can be used to refine the identification of low-risk patients.
In addition to supporting Kamel's analysis with an alternative model based on clinical data, our analysis was performed for a different healthcare system (i.e., the German).
In case of colinearity (P > 0.6), the most informative variable was selected for inclusion in the model, based on clinical arguments and Akaike information criterion [ 31].
