repository
Special Graduate Schools
Graduate School of Science and Technology
Department of Industrial Engineering
3. Theses(Master)
유비쿼터스 환경에서 적응형 하이브리드 시스템을 이용한 임상진단 및 예측 지원 시스템
| DC Field | Value | Language |
|---|---|---|
| dc.contributor.advisor | 왕지남 | - |
| dc.contributor.author | 정인성 | - |
| dc.date.accessioned | 2018-11-08T07:48:44Z | - |
| dc.date.available | 2018-11-08T07:48:44Z | - |
| dc.date.issued | 2007-02 | - |
| dc.identifier.other | 2147 | - |
| dc.identifier.uri | https://dspace.ajou.ac.kr/handle/2018.oak/7220 | - |
| dc.description | 학위논문(박사)--아주대학교 일반대학원 :산업공학과,2007. 2 | - |
| dc.description.tableofcontents | Ⅰ. INTRODUCTION = 1 A. Research Motivation = 1 B. Problem Statement and Research Objective = 2 C. Organization of Dissertation = 6 Ⅱ. LITERATURE SURVEY = 7 A. Medical Domain knowledge = 7 B. CDSS and AGENT system = 9 Ⅲ. ARTIFICIAL NEURAL NETWORK DESIGN = 15 A. Introduction = 15 B. Supervised learning = 16 C. Unsupervised learning / Self- organized learning = 27 D. Reinforcement learning = 41 Ⅳ. AN ADAPTIVE HYBRID CLINICAL DECISION AND PREDICTION SUPPORT SYSTEM = 49 A. Introduction = 49 B. Clinical decision and prediction support System framework = 53 C. Complementary two-phase prediction neural network model = 60 D. Adaptive hybrid neural network model for diagnosis = 84 Ⅴ. MODEL EVALUATION AND RESULT = 93 A. Introduction = 93 B. Prediction algorithm evaluation = 95 C. CDSS algorithm evaluation = 101 Ⅵ. APPLICATION OF CLINICAL DECISION SUPPORT AND PREDICTION SYSTEM = 110 A. Introduction = 110 B. Medical Domain Knowledge = 111 C. Medical Homecare Decision Support System = 116 Ⅶ. CONCLUSION = 127 A. Conclusion = 127 B. Limitation of this research and future work = 129 Ⅷ REFERENCE = 130 ABSTRACT = 135 | - |
| dc.language.iso | eng | - |
| dc.publisher | The Graduate School, Ajou University | - |
| dc.rights | 아주대학교 논문은 저작권에 의해 보호받습니다. | - |
| dc.title | 유비쿼터스 환경에서 적응형 하이브리드 시스템을 이용한 임상진단 및 예측 지원 시스템 | - |
| dc.type | Thesis | - |
| dc.contributor.affiliation | 아주대학교 일반대학원 | - |
| dc.contributor.department | 일반대학원 산업공학과 | - |
| dc.date.awarded | 2007. 2 | - |
| dc.description.degree | Master | - |
| dc.identifier.localId | 565619 | - |
| dc.identifier.url | http://dcoll.ajou.ac.kr:9080/dcollection/jsp/common/DcLoOrgPer.jsp?sItemId=000000002147 | - |
| dc.description.alternativeAbstract | Modern medical service systems are lacking in terms of offering real-time patient monitoring, diagnosis, and early detection of disease symptoms and problems in patient’s health state. The objective of this paper was to design a model of adaptive hybrid clinical decision and prediction support system under a ubiquitous environment for well-being life care, particularly related to high-risk disease and metabolic syndrome with noninvasive data such as vital signal data, food nutrient data, and activity data using a neural network. The model is designed using the complementary two-phase reverse neural network for being covered measure the problems of standard MLP model, which are local optimum, difficulty in modifying small sample size data, and one-direction learning. We determined that most of the simulation cases were satisfied by the two-phase reverse prediction neural network. In particular, small sample size of times series were more accurate than the standard MLP model. The research suggests the best prescription for prevention of diseases related to metabolic syndrome and high risk disease in the U-hospital, home healthcare system, PERS (Personal Emergency Response System), and silver town healthcare for elder people and patients. | - |
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