Publications

Processing and representation of meta-data for sleep apnea diagnosis with an artificial intelligence approach

Publication Type:

Journal Article

Authors:

David F. Nettleton; Muñiz, J

Source:

International Journal of Medical Informatics, Elsevier, Volume 63, Issue 1-2, p.77-89 (2001)

Keywords:

Questionnaire screening; Sleep apnea diagnosis; Relevance and reliability weights; Aggregation; Grade of membership; Categorical and scalar representation

Abstract:

In this article, we revise and try to resolve some of the problems inherent in questionnaire screening of sleep apnea cases and apnea diagnosis based on attributes which are relevant and reliable. We present a way of learning information about the relevance of the data, comparing this with the de?nition of the information by the medical
expert. We generate a predictive data model using a data aggregation operator which takes relevance and reliability information about the data into account to produce a diagnosis for each case. We also introduce a grade of membership for each question response which allows the patient to indicate a level of con?dence or doubt in their
own judgement. The method is tested with data collected from patients in a Sleep Clinic using questionnaires specially designed for the study. Other arti?cial intelligence predictive modeling algorithms are also tested on the same data and their predictive accuracy compared to that of the aggregation operator.

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