Telemedicine Monitoring System on PAP Adherence-Fox et al lenge is maximizing adherence with therapy. Many patients discontinue or do not use the device substantially. This is of clinical importance as increased adherence is associated with a reduced risk of motor vehicle crashes, improved cardiovascular outcomes, increased alertness, and improved quality of life. [3][4][5] Optimizing adherence is thus an important aspect of patient management.Adherence to PAP therapy is influenced by many factors, including severity of the disorder, side effects, therapeutic response, claustrophobia, patient's perception of disease seriousness, family support, and cost. Increased air leak with auto-PAP therapy is associated with reduced adherence, 6 and general management with interventions such as heated humidification, mask optimization, and topical nasal therapy improves adherence. 7 Adherence can also be significantly improved by a comprehensive support program and timely interventions by health professionals. This suggests that technical innovations that permit close monitoring of physiologic variables (such as air leak) during therapy and rapid troubleshooting of potential problems may improve adherence to PAP therapy. 8 Telemedicine can be defined as "the use of information and communication technology to deliver health services, exper-

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Clinical prediction rules play an important role in medical practice. They expedite diagnosis and limit unnecessary tests. However, the rule creation process is time consuming and expensive. With the current developments of efficient data mining algorithms and growing accessibility to medical data, the creation of clinical rules can be supported by automated rule induction from data. A data-driven method based on the reuse of previously collected medical records and clinical trial statistics is cost-effective; however, it requires well defined and intelligent methods for data analysis. This paper presents a new framework for knowledge representation for secondary data analysis and for generation of a new typicality measure, which integrates medical knowledge into statistical analysis. The framework is based on a semiotic approach for contextual knowledge and fuzzy logic for approximate knowledge. This semio-fuzzy framework has been applied to the analysis of predictors for the diagnosis of obstructive sleep apnea. This approach was tested on two clinical data sets. Medical knowledge was represented by a set of facts and fuzzy rules, and used to perform statistical analysis. Statistical methods provided several candidate outliers. Our new typicality measure identified those, which were medically significant, in the sense that the removal of those important outliers improved the descriptive model. This is a critical preprocessing step towards automated induction of predictive rules from data. These experimental results demonstrate that knowledge-based methods integrated with statistical approaches provide a practical framework to support the generation of clinical prediction rules.

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