A data preprocessing framework for supporting probability-learning in dynamic decision modeling in medicine.

Data preprocessing is needed when real-life clinical databases are used as the data sources to learn the probabilities for dynamic decision models. Data preprocessing is challenging as it involves extensive manual effort and time in developing the data operation scripts. This paper presents a framework to facilitate automated and interactive generation of the problem-specific data preprocessing scripts. The framework has three major components: 1) A model parser that parses the decision model definition, 2) A graphical user interface that facilitates the interaction between the user and the system, and 3) A script generator that automatically generates the specific database scripts for the data preprocessing. We have implemented a prototype system of the framework and evaluated its effectiveness via a case study in the clinical domain. Preliminary results demonstrate the practical promise of the framework.