|Títol||Processing and representation of meta-data for sleep apnea diagnosis with an artificial intelligence approach|
|Publication Type||Journal Article|
|Year of Publication||2001|
|Authors||Nettleton D, Muñiz J|
|Journal||International Journal of Medical Informatics|
|Paraules clau||Aggregation, Categorical and scalar representation, Grade of membership, Questionnaire screening, Relevance and reliability weights, Sleep apnea diagnosis|
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.
- Quant a IIIA