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

Authors:
Jürgen Paetz;Rüdiger W. Brause
Affiliations:
-;-
Venue:
ISMDA '01 Proceedings of the Second International Symposium on Medical Data Analysis
Year:
2001

Citing 5
Cited 1

Mining frequent patterns without candidate generation

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VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
About the Analysis of Septic Shock Patient Data

ISMDA '00 Proceedings of the First International Symposium on Medical Data Analysis
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ICTAI '99 Proceedings of the 11th IEEE International Conference on Tools with Artificial Intelligence

Knowledge-based approach to septic shock patient data using a neural network with trapezoidal activation functions

Artificial Intelligence in Medicine

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Abstract

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.