Statistical Methods - Text Mining Techniques for Healthcare Provider Quality Determination - page 110

Chapter 4
Predictive Modeling
Versus Regression
IntroductIon
Predictive modeling includes regression, both logistic and linear, depending upon the type of outcome
variable. It can also include the generalized linear model. However, there are other types of models also
available, including decision trees and artificial neural networks under the general term of predictive
modeling. Predictive modeling includes nearest neighbor discriminant analysis, also known as memory
based reasoning. These other models are nonparametric and do not require that you know the probability
distribution of the underlying patient population. Therefore, they are much more flexible when used
to examine patient outcomes. Because predictive modeling uses regression in addition to these other
models, the end results will improve upon those found using just regression by itself.
Some, but not all, of the predictive models require that all of the x-variables are independent. There-
fore, we can allow some dependency in the indicator functions for diagnosis codes. However, predictive
models must still also generally assume the uniformity of data entry. Because of the flexibility in the
use of variables to define confounding factors, we can consider the presence or absence of uniformity
in the model itself. We can define a variable to model provider, and to see how the provider impacts the
severity index.