Questionnaire screening of sleep apnea cases using fuzzy knowledge representation and intelligent aggregation techniques
Publication Type:
Conference ProceedingsSource:
Intelligent Data Analysis in Medicine and Pharmacology, IDAPMAP '99, Washington, United States., p.91-102 (1999)Keywords:
fuzzy representation; sleep apnea diagnosis; questionnaire; aggregationAbstract:
In this article, joint medical and data analysis expertise is brought to bear using fuzzy knowledge representation and ‘intelligent’ aggregation techniques to solve a difficult medical diagnosis problem,
that of sleep apnea syndrome screening
Evaluating reliability and relevance for WOWA aggregation of Sleep Apnea case data
Publication Type:
Conference ProceedingsSource:
Congress of the European Society of Fuzzy Logic and Technology - EUSFLAT '99, Palma de Mallorca, p.283-286 (1999)Keywords:
sleep apnea diagnosis; questionnaire responses; WOWA aggregation; clustering; classification; reliability; relevance.Abstract:
In this article, joint medical and data
analysis expertise is brought to bear using
contrasting data analysis methods and the
WOWA aggregation operator to solve a
difficult medical diagnosis problem, that of
sleep apnea syndrome screening. We
describe a method of calculating the
relevance and reliability weights used by
the WOWA operator.
Processing and representation of meta-data for sleep apnea diagnosis with an artificial intelligence approach
Publication Type:
Journal ArticleSource:
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 representationAbstract:
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.
