隨著網際網路的發達及普及，現今有許多人非常依賴醫療網站查詢醫療資訊，甚至會利用醫療網站進行線上諮詢，但有些病症上問題，過去可能有人提問過，導致這些重複的問答會有浪費醫療資源的情況；此外，大多數醫療網站所提供的資源，在網站呈現設計上過於複雜，導致使用者再查找資料或是想確認該就診哪一個科別，就會顯得毫無效率，非常耗時。
本研究選定衛生福利部「台灣e院」醫療諮詢網站案例為分析內容，以文字探勘技術，蒐集並擷取病症關鍵字，設計建置一套「智慧醫療網站問診科別服務推薦設計系統」。系統分為三步驟，以引導方式及不需輸入任何文字情況下，帶領線上使用者完成基本資料與自身病症選擇，最後依據這二個步驟的設定，計算病症關鍵字出現在每個科別字頻次數，經分析比對後得到科別推薦及相似案例呈現。
本系統以預期理論為基礎做為系統驗證模式，採用問卷調查法，蒐集使用者在使用實驗組系統（智慧醫療網站問診科別服務推薦設計系統）與對照組（台灣e院）後的操作感受，觀察使用者在「感知易用性」、「自我效能」、「預期成果/感知有用性」等三個構面之間是否存在差異。經分析結果證實，本研究建置出的智慧醫療推薦系統相較於對照組系統，確實在這三個構面都有更好的表現，顯著提高使用者在使用本系統後相信自我線上醫療問題查詢的能力與自我預期得到有用的查詢結果，進而驗證本系統的適用性。

With the rapid growth of the Internet technology, a great many health information websites emerges along with the popular use of these websites by online information searchers. Several websites offer online healthcare consultation services. Unfortunately, most of such websites do not consider the potential waste of healthcare professional resources with repetitive similar queries posed by searchers, nor do they provide simple and logical design for searchers to efficiently and effectively decide which medical division(s) to look up based on their current health status.
In this study, we consider designing the intelligent healthcare website on medical division recommendation founded upon Taiwan e-Doctor consulting cases. With text mining techniques to extract symptom keyword clusters, we propose a three-phase design process which involves personal data selection, symptom(s) identification facilitated with keyword clusters, and medical division recommendations based on the occurrence frequency of symptom clusters as well as optional referencing case representations. This process is designed in a logical guiding selection sequence without requesting searchers’ compulsory inputs.
A prototype website is developed to examine our proposed design. We employ expectancy theory to examine the prototype website on the constructs of “perceived ease of use”, “self-efficacy”, and “outcome expectancy/usefulness” with questionnaire survey method. Compared to Taiwan e-Doctor, the prototype appears to deliver significantly better performance in all three constructs. We conclude that the proposed design is able to enhance online information searchers’ belief of his/her ability to perform online search, and their expectations of the search outcome usefulness. The analytical results validate the feasibility of the proposed design accordingly.

論文審定書 i
誌謝 ii
摘要 iii
Abstract iv
圖次 viii
表次 ix
1. 緒論 1
1.1 研究背景 1
1.2 研究動機 1
1.3 研究目的 3
2. 文獻探討及回顧 4
2.1 文字探勘於醫療上的研究與運用 4
2.2 醫療專業術語與一般用詞的落差 4
2.3 友善系統操作介面 5
3. 研究方法 6
3.1 資料前處理 6
3.1.1 資料來源 6
3.1.2 資料選定 7
3.1.3 資料獲取 8
3.2 病症關鍵字處理 8
3.2.1 關鍵字識別 8
3.2.2 關鍵字彙整 9
3.3 資料後處理 10
3.3.1 常見問答斷詞 10
3.3.2 計算病症次數 10
3.4 系統實作 10
3.4.1 第一步驟：填寫基本資料 11
3.4.2 第二步驟：選擇身體不適症狀 12
3.4.3 第三步驟：建議就診科別 15
4. 實驗與結果 16
4.1 驗證理論 16
4.1.1 科技接受模型 16
4.1.2 自我效能 16
4.1.3 結果預期/有用性 17
4.2 實驗設計 17
4.2.1 實驗組與對照組之設計 18
4.2.2 實驗假設 19
4.2.3 驗證方法 20
4.2.4 問卷設計 20
4.3 理論驗證結果 21
4.3.1 測試與問卷回收 21
4.3.2 信度檢驗 22
4.3.3 效度檢驗 24
4.4 假設驗證 25
5. 彙總與討論 27
5.1 總結 27
5.2 未來方向 28
參考文獻 29
附錄一 34
附錄二 37

[1] AHIMA Computer-Assisted Coding e-HIM Work Group. (2004). Delving into computer-assisted coding. Journal of AHIMA/American Health Information Management Association, 75(10), 48A.
[2] Adlassnig, K. P. (2009, August). Automatic mapping of clinical documentation to SNOMED CT. In Medical Informatics in a United and Healthy Europe: Proceedings of MIE 2009, the XXII International Congress of the European Federation for Medical Informatics (Vol. 150, p. 228). IOS Press.
[3] Baker-Eveleth, L., & Stone, R. W. (2008). Expectancy theory and behavioral intentions to use computer applications. Interdisciplinary Journal of Information, Knowledge, and Management, 3, 135-146.
[4] Bandura, A. (1977). Self-efficacy: toward a unifying theory of behavioral change. Psychological review, 84(2), 191.
[5] Bandura, A. (1986). Social foundations of thought and action: A social cognitive theory. Englewood Cliffs, NJ, 1986.
[6] Chen, N. S., Huang, H. Y., & Shih, Y. C. (2002, December). Factors affecting usage of web-based teachers' training in elementary and high school. In Computers in Education, 2002. Proceedings. International Conference on (pp. 589-592). IEEE.
[7] Compeau, D. R., & Higgins, C. A. (1995). Application of social cognitive theory to training for computer skills. Information systems research, 6(2), 118-143.
[8] Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS quarterly, 319-340.
[9] Downey, J. (2006, January). Measuring general computer self-efficacy: The surprising comparison of three instruments in predicting performance, attitudes, and usage. In System Sciences, 2006. HICSS'06. Proceedings of the 39th Annual Hawaii International Conference on (Vol. 8, pp. 210a-210a). IEEE.
[10] Grandon, E. E., Alshare, K., & Kwun, O. (2005). Factors influencing student intention to adopt online classes: A cross-cultural study. Journal of Computing Sciences in Colleges, 20(4), 46-56.
[11] Hernandez, B., Jimenez, J., & Jose Martin, M. (2008). The impact of self-efficacy, ease of use and usefulness on e-purchasing: An analysis of experienced e-shoppers. Interacting with Computers, 21(1-2), 146-156.
[12] Henry, J. W., & Stone, R. W. (1995). Computer self-efficacy and outcome expectancy: the effects on the end-user's job satisfaction. ACM SIGCPR computer personnel, 16(4), 15-34.
[13] Hersh, W. R., & Hickam, D. (1995). Information retrieval in medicine: the SAPHIRE experience. Journal of the American Society for Information Science (1986-1998), 46(10), 743.
[14] Hina, S., Atwell, E., & Johnson, O. (2010). Semantic tagging of medical narratives with top level concepts from SNOMED CT healthcare data standard. Int. J. Intell. Comput. Res, 2, 204-210.
[15] Holden, H., & Rada, R. (2011). Understanding the influence of perceived usability and technology self-efficacy on teachers’ technology acceptance. Journal of Research on Technology in Education, 43(4), 343-367.
[16] Hsu, W. (2013). Bridging the vocabulary gap for EFL medical undergraduates: The establishment of a medical word list. Language Teaching Research, 17(4), 454-484.
[17] Karahanna, E., Agarwal, R., & Angst, C. M. (2006). Reconceptualizing compatibility beliefs in technology acceptance research. MIS quarterly, 781-804.
[18] Kuo, Y. H., Lee, C. S., Guo, S. M., & Tu, F. T. (2004). Apply FNN Model to Construct Ontology-based Q&A System. WSEAS Transactions on Communications, 3(1), 328-335.
[19] Lai, M. L. (2008). Technology readiness, internet self-efficacy and computing experience of professional accounting students. Campus-Wide Information Systems, 25(1), 18-29.
[20] Ma, Q., & Liu, L. (2003). The role of internet self-efficacy in accepting web-based medical records. AMCIS 2003 Proceedings, 110.
[21] Nie, L., Zhao, Y. L., Akbari, M., Shen, J., & Chua, T. S. (2015). Bridging the vocabulary gap between health seekers and healthcare knowledge. IEEE Transactions on Knowledge and Data Engineering, 27(2), 396-409.
[22] Park, S. Y. (2009). An analysis of the technology acceptance model in understanding university students' behavioral intention to use e-learning. Educational technology & society, 12(3), 150-162.
[23] Park, Y., & Chen, J. V. (2007). Acceptance and adoption of the innovative use of smartphone. Industrial Management & Data Systems, 107(9), 1349-1365.
[24] Patrick, J., Wang, Y., & Budd, P. (2007, January). An automated system for conversion of clinical notes into SNOMED clinical terminology. In Proceedings of the fifth Australasian symposium on ACSW frontiers-Volume 68 (pp. 219-226). Australian Computer Society, Inc.
[25] Plouffe, C. R., Hulland, J. S., & Vandenbosch, M. (2001). Richness versus parsimony in modeling technology adoption decisions—understanding merchant adoption of a smart card-based payment system. Information systems research, 12(2), 208-222.
[26] Sang, E. T. K., Bouma, G., & De Rijke, M. (2005, July). Developing offline strategies for answering medical questions. In Proceedings of the AAAI-05 Workshop on Question Answering in Restricted Domains, Pittsburgh, PA, USA (pp. 41-45).
[27] Strong, D. M., Dishaw, M. T., & Bandy, D. B. (2006). Extending task technology fit with computer self-efficacy. ACM SIGMIS Database: the DATABASE for Advances in Information Systems, 37(2-3), 96-107.
[28] Surendran, P. (2012). Technology acceptance model: A survey of literature. International Journal of Business and Social Research, 2(4), 175-178.
[29] Venkatesh, V., & Davis, F. D. (1996). A model of the antecedents of perceived ease of use: Development and test. Decision sciences, 27(3), 451-481.
[30] Venkatesh, V., & Davis, F. D. (2000). A theoretical extension of the technology acceptance model: Four longitudinal field studies. Management science, 46(2), 186-204.
[31] Wang, S. H. (2016). Ontology-Based Semantic Q&A system in Health Care: An Illustrated Application on Down Syndrome.
[32] Zhang, Y. (2013). Toward a layered model of context for health information searching: an analysis of consumer-generated questions. Journal of the American Society for Information Science and Technology, 64(6), 1158-1172.
[33] Jacob Nielsen (1994) Usability Inspection Methods
[34] 林玉玟. (2013)，從可用性, 知覺有用性, 知覺易用性, 與組織學習對 ERP 系統績效之影響 (Doctoral dissertation, 撰者)
[35] 劉姿蘭 (2009)，應用文字探勘技術於疾病分類自動編碼之研究
[36] 彭俊凡 (2017)，運用文件分群與文字探勘技術於生物醫學文獻分析之應用研究