近年來，產、官、學界逐漸重視民眾健康資訊的管理與應用，加上消費者自主意識的提高，促使醫院加速進行醫療資訊化，以提升醫療品質及效率，同時滿足就醫者對醫療相關資訊與決策支援的需求。此外，網際網路技術的快速發展、健康保健觀念及醫療資訊政策普及化所帶來的影響，也使得提供線上即時、個人化、智慧型的健檢決策支援系統，成為了一個關鍵的研究與實務應用課題。目前大多數的健康檢查中心因主、客觀的限制，只能提供就醫者套餐式健檢組合，供就醫者作為健康檢查參考。依醫者雖可選擇健檢服務客製化模式，就個人需要或喜好增減檢查項目，但仍無法滿足個人化健檢服務需求，亦即得到專業推薦且符合個人差異與需要的個人化健檢組合方案。再者，過去的健檢系統除缺乏個人化健檢推薦功能，對於個人保健建議及後續就醫服務等亦缺乏整合性服務。而以往醫療資訊化過程中，因缺乏醫療資訊標準格式之發展，也限制醫療資訊在院所間之交換與流通，造成整合性醫療服務的困難。面對上述醫療資訊不對稱及個人化保健與醫療系統支援環境缺乏的困難，整合資料、模式及知識技術的個人化智慧型健檢決策支援系統，成為一個可行且重要的解決方法。由於此課題在文獻上缺乏具體成果，本研究的目的即在提出一個全球資訊網環境中個人化智慧型健檢決策支援系統 (Personalized Intelligent Decision Support System for Health Examination, PIDSSHE)的架構及流程，系統功能著重於個人化健檢、保健及醫療等方案的推薦，以及智慧型輔助診斷和即時性醫療資訊交換等。本論文也將提出系統發展方法並建置原型系統，以驗證架構、流程及方法的可行性與效益。 / In recent years, the health industry, the government as well as the academic area pay more attention to the people’s health information management and applications. Due to the promotion of consumer health information, the proliferation of Internet, the awareness of healthcare, and the policy of mdical care information, how to provide online personalized intelligent healthcare decision support systems becomes an critical academic and applied research issue. Most health exam centers only offer health exam packages to the patients due to subjective or objective restrictions. Even the patients may customize their health exams by adding or dropping certain health exam items, it does not meet the needs of personalized health exam recommendation based on individual differences and personal needs. Moreover, there are no integrated services for follow-up personalized clinical care recommendations. The exchanges of medical information between hospitals are limited due to the lacks of medical information exchange standards. In turn, it leads to the gaps of integrated medical services. Facing the medical information assymetry and the difficulties of current medical supporting environment, an intelligent decision support system for personalized healthcare based on the integration of data, models, and knowledge is a feasible and important solution. To fill the research gaps, our research proposes a web-based intelligent decision support system for personalized health care. To fill the research gaps, our research is to propose the system architecture and processes of a Web-based intelligent decision support systems for personalized healthcare. We also illustrate our system development methodology as well as our prototype to validate the feasibility and effectiveness of our system architecture, processes and methodology.

傳統的資產負債管理(Asset-Liability Management，ALM)研究大多強調數量分析方法，並未考慮資料來源的問題。然而在銀行實務上，資產負債管理人員卻必須根據現有內外部資料來釐定資產負債組合的整体政策。在決策支援系統中，模式整合的功能包含模式之組合及連結等，可用以整合數量分析模式與相關資料。本研究運用人工智慧技術來探討資產負債管理中模式整合之問題。藉此可以明瞭ALM的分析流程，以作為銀行人員訓練之參考。另一方面由於應用黑板架構發展系統，也可以提供一個有彈性的整合環境，以反應使用者需求及資料異動狀況，亦可彈性新增、刪除及修改模式整合過程中的資料結構與知識內涵，以為未來連接理論技巧與實務環境之參考。 / The computer support for Asset Liability Management (ALM) in the literature emphasizes on the mathematical analysis and does not address the data source problems. In the practical banking environment, however, ALM decisions are made based on the dynamic internal and external data changes. Therefore, an ideal ALM decision support system has to consider the integration of data sources and mathematical analysis. Traditional Decision Support Systems (DSS) rely on the expert's assistance to understand the problem and formulate or integrate appropriate models. There is a growing recognition that incorporates Artificial Intelligence techniques (Al) into the DSS can enhance the acceptance of these decision aids by management. 　　This paper intends to develop an Intelligent Decision Support System (TDSS) and addresses the model integration concept for the ALM. In the paper, model integration is defined as a series of processes from which important decision making information is inferred through automatic data model mapping and mathematical model conversion. The investigation of model integration concept helps the ALM analysis process understanding which can be useful for baaldng personnel training. On the other hand, the IDSS provides a flexible integration environment in which the system can flexibly response to the user's analysis request with the updated data situations. Since the blackboard architecture used for the system development supports the modularization structure, its inherent maintainability aLows a flexible update of the domain knowledge and data structure, and can therefore serve as a testbed to evaluate the potential integration approaches of various ALM data and mathematical models.

智慧型決策支援系統(Intellignet Decision Support Systems)的特點是可以同時處理定性和定量資料於同一個系統中，以同時執行各種知識推論及數量模式之運算。黑板架構(Blackboard Architecture)的做法是將決策支援求解過程的資料、模式和知識運用情形記錄於一共同工作區─稱之為黑板(Blackboard)，將模式及知識記錄在所謂的知識源(Knowledge Sources)中，並提供較有彈性之控制機制，應可提供較佳的解釋功能。以黑板架構為基礎的智慧型系統多應用在科學及工程方面，在管理方面卻寥寥無幾；管理問題多半屬於半結構性或非結構性，良好的解釋應為智慧型決策系統之重要功能。本研究擬就銀行業之授信審查做為本系統之專業領域知識(Domain Knowledge)，運用黑板架構中的階層化問題表現方式及模組化知識源分類之特性，建立提供完善解釋功能之智慧型授信決策支援系統。 / Incorporating artificial intelligence (AI) technique is critical to improve the functionality of decision support systems. Explanation function for consultation-based systems has been emphasized in the literature and should be considered important in developing intelligent decision support systems. Blackboard architecture can support a well-organized explanation facility due to its structurization of problem solving space, modularization of domain knowledge, and flexibility of reasoning control. Applying blackboard systems to managerial domain gets attention recently. Since most managerial consultation problems are unstructured or semi-structured, good explanation facility should be able to enhance the consultation effectiveness. The thesis investigates the potential of developing an explanation facility on the blackboard architecture using the loan evaluation as an example. During the interactive consultation process, the system can answer questions such as "What?", "Why?", "How?", and "Where?" with a friendly user interface. In terms of contribution, the inclusion of explanation facility can potentially increase the willingness and confidence of decision makers in using intelligent decision support systems. On the other hand, applying the graphic user interface to the development of explanation facility based on the blackboard architecture can make the reasoning process transparent and enhance the acceptance of this AI technique to managerial problem solving.