By Katarzyna A. Tarnowska, Zbigniew W. Ras, Pawel J. Jastreboff
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Extra info for Decision Support System for Diagnosis and Treatment of Hearing Disorders
Data collected includes demographic information, medical information (on pharmaceuticals taken and audiological measurements) and forms. • Storing the data in the central database of medical facility. • Performing characterization of tinnitus based on data collected from a patient at a visit and prediction models built on historical data. • Advising in treatment actions with the use of rule engine facility. Patients should, on the other hand, fill out electronic forms (initial/follow-up 54 5 RECTIN System Design Fig.
Performing characterization of tinnitus based on data collected from a patient at a visit and prediction models built on historical data. • Advising in treatment actions with the use of rule engine facility. Patients should, on the other hand, fill out electronic forms (initial/follow-up 54 5 RECTIN System Design Fig. 1 Use cases for RECTIN system forms and Newman form), based on paper version as in Appendix A, B and C. However, when confronted with our medical practice, these forms are actually serving as a help to perform structural interviews and patients should not be doing them.
In a dataset, presented in Fig. 2, decision attribute would be similar as in Table in Fig. 1—whether a person is sick or not. 1). This attribute would classify objects (patients) into tinnitus treatment group, taking into account medical/audiological evaluation and form responses. For action rules extraction, it is also relevant to differentiate between so-called flexible attributes, which can be changed, and stable attributes [RW00], which cannot 38 4 Knowledge Discovery Approach for Recommendation be changed: A = A St ∪ A Fl , where A St and A Fl denote stable attributes and flexible attributes respectively.