指導教授：李瑞庭臺灣大學：資訊管理學研究所李孟修Lee, Meng-HsiuMeng-HsiuLee2014-11-292018-06-292014-11-292018-06-292014http://ntur.lib.ntu.edu.tw//handle/246246/263499隨著使用者對於社群網路平台的參與度越來越高,各種不同社群網路的應用也隨 之蓬勃發展。其中醫療保健網路社群已成為近年來被廣泛使用的一種社交網路的類型。 它提供使用者去分享他們關於疾病的經驗,同時也給研究者一個蒐集、分析資料的平 台。因此,本論文提出一個研究架構,從醫療社群網路探討病人疾病、症狀、療法、 有效性以及副作用之間的關係。所提出的研究架構包含三個階段,首先,我們修改了 Affinity propagation 方法,使用階層的方式將病人分群。並提出了一個加權的機制增 加具鑑別力卻較少見的特徵的重要度;然後,我們從分群的結果找出醫療頻繁型樣 (frequent pattern);最後,我們分析各個醫療特徵之間的關係。實驗結果顯示,我們的 方法可以將具有類似特徵的病人分群在一起,並提供病人可參考的資訊及幫助醫生做 進一步的分析。With more and more user involvements in social network platforms, many kinds of social network applications have been rapidly growth. Healthcare social networks have become a popular type of social networks recently. They provide platforms for users to share their experience and for researchers to collect and analyze the data. In this thesis, we propose a framework to investigate the relationships among medical features (conditions, symptoms, treatments, effectiveness and side effects) for different types of patients in the healthcare social network. The proposed framework contains three phases. First, we extract the medical features of conditions, symptoms, treatments, effectiveness and side effects for each patient in the healthcare social network. Next, we modify the affinity propagation method (AP) to cluster patient profiles in a hierarchical manner, where we design a weighed scheme to increase the importance of less frequent but significant medical features. Finally, we mine frequent medical patterns for each cluster, and analyze the patterns mined and patient profiles in the resultant clusters. The experiment results show that the proposed framework can cluster similar patient profiles together, provide patients some quick references and help doctors to conduct in-depth analyses.Contents i List of Figures ii List of Tables iii Chapter 1 Introduction 1 Chapter 2 Related Work 5 Chapter 3 Our Proposed Framework 7 3.1 Extracting Patient Profiles 7 3.2 Clustering Patient Profiles 9 3.2.1 Affinity propagation 10 3.2.2 Clustering patient profiles level by level 13 3.2.3 Clustering example 14 3.3 Analyzing Patient Profiles 15 Chapter 4 Experiments 19 4.1 Cluster and Pattern Analysis 19 4.2 Medical Features Analysis 24 4.2.1 Relationships between medical features 24 4.2.2 Possible treatments and future conditions 25 Chapter 5 Conclusions and Future Work 29 References 31921448 bytesapplication/pdf論文公開時間：2024/12/31論文使用權限：同意有償授權(權利金給回饋學校)醫療社群網路階層式分群頻繁型樣探勘thesishttp://ntur.lib.ntu.edu.tw/bitstream/246246/263499/1/ntu-103-R01725007-1.pdf