A Study on the Relationship of Clinical Manifestations in Sleep Disorder Patient

• Main Authors: Bai-Sheng Hsiao, 蕭百勝
• Other Authors: 徐建業
• Format: Others
• Language: zh-TW
• Published: 2009
• Online Access: http://ndltd.ncl.edu.tw/handle/79295506868872788712

碩士 === 臺北醫學大學 === 醫學資訊研究所 === 97 === Sleep is essential to human life, adequate and high quality of sleep is an important factor for good health. Insufficient sleep can be the causes of some chronic diseases and affect person''s concentration and mental state that may be connected to some of traffic accidents. In recent years, many domestic medical institutions have set up their own sleep medical center (SMC). In SMC, patients with sleep disorders are diagnosed based on the procedures of a serial of inspections. The measurement usually includes the patients basic information, self-administered questionnaire regarding sleep status, and the data generated by Polysomnography (PSG) from measuring the electrophysiological signals. The data we used in this study included the basic information of the patients, questionnaires, all night PSG summary and PSG inspection report. Because of the expensive equipments and the cost of the complex procedures, comprehensive clinical record obtained from patients with sleep disorder is not very common. Very few studies related to sleep disorder have adopted data mining methods on data analysis. In this study, we used Apriori algorithm (a method of association rule) to analyze the clinical data and try to find the relationship between the subjective (questionnaires) and objective (PSG Data) information. The significance of our results applied to clinical practice were also be discussed. We have build a knowledge architecture based on the association model and compared rules to the current understanding and evidences of sleep medicine. The results show that association is a suitable methods used for the analysis of sleep disorder clinical data. We believe that the results of this study will be beneficial to the sleep disorder patient in treatment and diagnosis as well as the clinician to get a more deep insight.