A Frequent Patterns Tree Approach for Rule Generation with Categorical Septic Shock Patient Data

摘要

In abdominal intensive care medicine letality of septic shock patients is very high. In this contribution we present results of a data driven rule generation with categorical septic shock patient data, collected from 1996 to 1999. Our descriptive approach includes preprocessing of data for rule generation and application of an efficient algorithm for frequent patterns generation. Performance of generated rules is rated by frequency and confidence measures. The best rules are presented. They provide new quantitative insight for physicians with regard to septic shock patient outcome.