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This predicted distribution is obtained by taking into account non-linear interactions between the predictive variables to evaluate the similarity between the instances of the dataset.
Clinical research examining the relationship between the predictive variables for neurologic adverse events and cerebral blood flow has suggested some surgical strategies for minimizing risk, such as limiting manipulation of the ascending aorta.
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There are several reasons why adding a new predictive variable may not result in a better accuracy of a predictive model including a low prevalence of the variable, the absence of predictive value of this variable, and the presence of a correlation between the predictive variable and the variable(s) originally included in the predictive model (i.e. APACHE-II).
The first step is to develop a model to establish the relationship between the response variable y and the predictive variables x's in U. Specifically, the first step is to develop a model to convert the multiple predictive variables x 1,…,x L in U to a univariate predictive score to order each patient's response, where L is the total number of x's in U.
The predictive variables formed the fixed effects of the model.
The 95% CIs for each variable were used to determine the relationships between the various predictive variables and outcomes.
However, we did not find a satisfied correlation coefficient between thyroid mass and 99mTc-pertechnetate uptake (r = 0.426), which indicated a relative independence between the two predictive variables for thyroid function after RIT.
Table 2 aims at establishing the relationship between predictive variables (including those related to the patient, surgery, and medical resources) and dependent variable (outcome).
Multivariate Cox proportional hazards regression models were used to estimate the association between predictive variables (those variables that were associated with the outcome in bivariate analyses with P <0.10) and premature AI discontinuation.
Table 4 shows odds ratios, confidence intervals, and p values for the association between predictive variables and 100-day mortality.
This interaction attenuated the association between MSDs and predictive variables when both variables were placed in the same model.
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