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http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/55207完整後設資料紀錄
| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 陳建錦 | |
| dc.contributor.author | Peng-Hsuan Lu | en |
| dc.contributor.author | 呂芃萱 | zh_TW |
| dc.date.accessioned | 2021-06-16T03:51:19Z | - |
| dc.date.available | 2020-01-20 | |
| dc.date.copyright | 2015-01-30 | |
| dc.date.issued | 2014 | |
| dc.date.submitted | 2015-01-19 | |
| dc.identifier.citation | 1. Fox, S., The Social Life of Health Information - 2011, in Pew Research Center’s Internet & American Life Project 2011.
2. Zickuhr, K., Generations 2010. Pew Internet & American Life Project, 2010. 16: p.11. 3. Fox, S. and M. Duggan, Health Online - 2013, in Pew Research Center’s Internet & American Life Project 2013. 4. Zhang, Y. A review of search interfaces in consumer health websites. in Workshop on Human-Computer Interaction and Information Retrieval. 2011. 5. Luo, G. and C. Tang. On iterative intelligent medical search. in Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval. 2008. ACM. 6. Smith, A., Smartphone Ownership - 2013, in Pew Research Center’s Internet & American Life Project 2013. 7. Fox, S. and M. Duggan, Mobile Health - 2012, in Pew Research Center’s Internet & American Life Project 2012. 8. Joachims, T. Optimizing search engines using clickthrough data. in Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining. 2002. ACM. 9. Joachims, T., et al. Accurately interpreting clickthrough data as implicit feedback. in Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval. 2005. ACM. 10. Radlinski, F. and T. Joachims. Active exploration for learning rankings from clickthrough data. in Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining. 2007. ACM. 11. Chen, H. and D. Zeng, AI for global disease surveillance. Intelligent Systems, IEEE, 2009. 24(6): p. 66-82. 12. Limsopatham, N., et al. Exploiting term dependence while handling negation in medical search. in Proceedings of the 35th international ACM SIGIR conference on Research and development in information retrieval. 2012. ACM. 13. Song, S.-k. and S.H. Myaeng, A novel term weighting scheme based on discrimination power obtained from past retrieval results. Information Processing & Management, 2012. 48(5): p. 919-930. 14. Hasan, M.A. and A.R. Chowdhury, Human Disease Diagnosis Using a Fuzzy Expert System. Journal of Computing, 2010. 2(6): p. 5. 15. Kadhim, M.A., M.A. Alam, and H. Kaur, Design and Implementation of Fuzzy Expert System for Back pain Diagnosis. International Journal of Innovative Technology & Creative Engineering, IJITCE, voU, 2011(9): p. 16-22. 16. Wiriyasuttiwong, W. and W. Narkbuakaew, Medical Knowledge-Based System for Diagnosis from Symptoms and Signs. International Journal of Applied Biomedical Engineering, 2009. 2. 17. Zhu, D. and B. Carterette. An Adaptive Evidence Weighting Method for Medical Record Search. in Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval. 2013. Dublin, Ireland. 18. Collins, D.R. and R.D. Collins, Algorithmic Diagnosis of Symptoms and Signs: A Cost-Effective Approach. 2012: Wolters Kluwer Health. 19. Luo, G., C. Tang, and S.B. Thomas, Intelligent personal health record: experience and open issues. Journal of medical systems, 2012. 36(4): p. 2111-2128. 20. Harter, S.P., A probabilistic approach to automatic keyword indexing. 1974, University of Chicago. 21. Amati, G. and C.J. Van Rijsbergen, Probabilistic models of information retrieval based on measuring the divergence from randomness. ACM Transactions on Information Systems (TOIS), 2002. 20(4): p. 357-389. 22. Macdonald, C. and I. Ounis. Voting for candidates: adapting data fusion techniques for an expert search task. in Proceedings of the 15th ACM international conference on Information and knowledge management. 2006. ACM. | |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/55207 | - |
| dc.description.abstract | 網際網路的普及以及近年來大眾對於健康議題的重視，使得健康相關的網站及系統逐漸的普遍。在本篇研究中，我們實作出一個健康照護搜尋引擎，希望能提供使用者完整、有效率的疾病診斷服務，我們應用一個創新的權重模型，其涵蓋獨特性及主要性兩種特性，並設定了三個參數來探討考量身體階層後的影響力。最後，我們以三百多個實際案例來做測試，實驗結果顯示考慮不同身體階層能讓系統的準確率明顯提升，而系統也表現了良好的診斷疾病效能。 | zh_TW |
| dc.description.abstract | Accessing the Internet and emphasizing health are two of the most popular trends in recent years. Several health websites or systems were being constructed one after another. In this paper, we implemented a healthcare inference engine for supporting users who has concerns about their own physical conditions. Our research applied a novel weighting model which based on two features of weight which were uniqueness and dominance. Moreover, three parameters were assigned to measure the effect from adding the hierarchy levels of body parts into our methods. In sum, great improvement could be seen after applying body parts hierarchy levels and this healthcare inference engine could performed successfully in real cases. | en |
| dc.description.provenance | Made available in DSpace on 2021-06-16T03:51:19Z (GMT). No. of bitstreams: 1 ntu-103-R01725041-1.pdf: 869268 bytes, checksum: 79cfd5636136007f2244572c2d24cc49 (MD5) Previous issue date: 2014 | en |
| dc.description.tableofcontents | 口試委員會審定書..........................................................i
誌謝.....................................................................ii 中文摘要................................................................iii ABSTRACT.................................................................iv LIST OF CONTENTS..........................................................v LIST OF FIGURES ........................................................ vi LIST OF TABLES..........................................................vii 1. Introduction ......................................................... 1 2. Related Work ......................................................... 4 2.1 Methods for Improving Search Results ................................ 5 2.2 Weighting Model ..................................................... 8 3. Methodology ......................................................... 10 3.1 Data Representation ................................................ 11 3.2 Weight Assignment .................................................. 12 3.3 Disease Similarity Calculation ..................................... 16 3.4 Interactive Diagnosis Procedure .................................... 18 4. Experiment .......................................................... 19 4.1 Dataset and evaluation metrics ..................................... 19 4.2 System component evaluation ...................................... 20 5. Case Study .......................................................... 22 6. Conclusion and future works ......................................... 24 REFERENCE .............................................................. 25 | |
| dc.language.iso | en | |
| dc.subject | 權重模型 | zh_TW |
| dc.subject | 搜尋引擎 | zh_TW |
| dc.subject | 健康照護 | zh_TW |
| dc.subject | weighting model | en |
| dc.subject | inference engine | en |
| dc.subject | healthcare | en |
| dc.title | 智慧型互動病症推論引擎 | zh_TW |
| dc.title | An Intelligent and Interactive Healthcare Inference Engine | en |
| dc.type | Thesis | |
| dc.date.schoolyear | 103-1 | |
| dc.description.degree | 碩士 | |
| dc.contributor.oralexamcommittee | 陳孟彰,蔡銘峰,盧信銘 | |
| dc.subject.keyword | 健康照護,搜尋引擎,權重模型, | zh_TW |
| dc.subject.keyword | healthcare,inference engine,weighting model, | en |
| dc.relation.page | 26 | |
| dc.rights.note | 有償授權 | |
| dc.date.accepted | 2015-01-19 | |
| dc.contributor.author-college | 管理學院 | zh_TW |
| dc.contributor.author-dept | 資訊管理學研究所 | zh_TW |
| 顯示於系所單位： | 資訊管理學系 | |
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