Knowledge-Based Induction of Clinical Prediction Rules

Knowledge-Based Induction of Clinical Prediction Rules

Mila Kwiatkowska (Thompson Rivers University, Canada), M. Stella Atkins (Simon Fraser University, Canada), Les Matthews (Thompson Rivers University, Canada), Najib T. Ayas (University of British Columbia, Canada) and C. Frank Ryan (University of British Columbia, Canada)
DOI: 10.4018/978-1-60566-218-3.ch017
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Abstract

This chapter describes how to integrate medical knowledge with purely inductive (data-driven) methods for the creation of clinical prediction rules. It addresses three issues: representation of medical knowledge, secondary analysis of medical data, and evaluation of automatically induced predictive models in the context of existing knowledge. To address the complexity of the domain knowledge, the authors have introduced a semio-fuzzy framework, which has its theoretical foundations in semiotics and fuzzy logic. This integrative framework has been applied to the creation of clinical prediction rules for the diagnosis of obstructive sleep apnea, a serious and under-diagnosed respiratory disorder. The authors use a semio-fuzzy approach (1) to construct a knowledge base for the definition of diagnostic criteria, predictors, and existing prediction rules; (2) to describe and analyze data sets used in the data mining process; and (3) to interpret the induced models in terms of confirmation, contradiction, and contribution to existing knowledge.
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Background

In this section, we briefly describe the etiology, diagnosis, and treatment of obstructive sleep apnea. Next, we discuss the role of clinical prediction rules in medicine and describe their creation process. Last, we discuss problems related to secondary use of medical data.

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