請用此 Handle URI 來引用此文件:
http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/71568
完整後設資料紀錄
DC 欄位 | 值 | 語言 |
---|---|---|
dc.contributor.advisor | 余俊瑜 | |
dc.contributor.author | Yu-Chun Huang | en |
dc.contributor.author | 黃羽均 | zh_TW |
dc.date.accessioned | 2021-06-17T06:03:30Z | - |
dc.date.available | 2021-01-30 | |
dc.date.copyright | 2019-01-30 | |
dc.date.issued | 2019 | |
dc.date.submitted | 2019-01-28 | |
dc.identifier.citation | AntheunisL.Marjolijn, TatesKiek, & NieboerETheodoor. (2013). Patients' and health professionals' use of social media in health care: motives, barriers and expectations. Patient education and counseling.
ArunR., SureshV., MadhavanE. VeniC., & MurthyN. NarasimhaM. (2010). On Finding the Natural Number of Topics with Latent Dirichlet Allocation: Some Observations. 14th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining. Benetoli, A., Chen., T., & Aslani, P. (2017). How patients’use of social media impacts their interactions with healthcare professionals. Patient Education and Counseling BleiM.David. (2012). Probabilistic topic models. Communications of the ACM . BradyEllen, SegarJulia, & SandersCaroline. (2016). “You get to know the people and whether they’re talking sense or not”: Negotiating trust on health-related forums. CChristopherYang, HaodongYang, LingJiang, & MiZhang. (2012). Social media mining for drug safety signal detection. In Proceedings of the 2012 international workshop on Smart health and wellbeing. ChoudhuryDeMunmun, GamonMichael, CountsScott, & HorvitzEric. (2013). Predicting Depression via Social Media. Proceedings of the Seventh International AAAI Conference on Weblogs and Social Media . ChoudhuryDeMunmun, GamonMichael, CountsScott, & HorvitzEric. (2013). Predicting Depression via Social Media. Proceedings of the Seventh International AAAI Conference on Weblogs and Social Media. ChretienC.Katherine, & KindTerry. (2013). Social Media and Clinical Care : Ethical, Professional, and Social Implications. American Heart Association, Inc. ChretienC.Katherine, & KindTerry. (2013). Social Media and Clinical Care Ethical, Professional, and Social Implications. American Heart Association, Inc. ChuangKatherine, & YangC.Christopher. (2010). Social Support in Online Healthcare Social Networking. iConference . David M. BleiY. Ng, Michael I. JordanAndrew. (2003). Latent Dirichlet Allocation. Journal of machine Learning research. DeneckeK., BamidiP., C. BondE, Gabarron, HousehM., LauY. S.A., . . . HansenM. (2015). Ethical Issues of Social Media Usage in Healthcare. Yearb Med Inform . Derek GreeneO’Callaghan, P ́adraig CunninghamDerek. (2014). How Many Topics? Stability Analysis for Topic Models. Joint European Conference on Machine Learning and Knowledge Discovery in Databases. DredzeMark. (2012). How Social Media Will Change Public Health. IEEE . Felix GreavesRamirez-Cano, Christopher Millett, Ara Darzi, Liam DonaldsonDaniel. (2013). Harnessing the cloud of patient experience: using social media to detect poor quality healthcare. BMJ Qual Sa. FrostHJeana, & MassagliPMichael. (2008). Social Uses of Personal Health Information Within PatientsLikeMe, an Online Patient Community: What Can Happen When Patients Have Access to One Another’s Data. J Med Internet Res. . GholamiJaleh, HosseiniHamedSayed, AshoorkhaniMahnaz, & MajdzadehReza. (2011). Lessons Learned from H1N1 Epidemic: The Role of Mass Media in Informing Physicians. Int J Prev Med. GreeneA.Jeremy, ChoudhryK.Niteesh, KilabukElaine, & ShrankH.William. (2011). Online Social Networking by Patients with Diabetes: A Qualitative Evaluation of Communication with Facebook. Journal of General Internal Medicine. GuntherEysenbach, JohnPowell, MarinaEnglesakis, CarlosRizo, & AnitaStern. (2004). Health related virtual communities and electronic support groups: systematic review of the effects of online peer to peer interactions. BMJ. Hanna M. WallachMurray, Ruslan Salakhutdinov, David MimnoIain. (2009). Evaluation Methods for Topic Models. ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning. JBai, J.-YNie, GCao, & H.Bouchard. (2007). Using Query Contexts in Information Retrieval. SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval. JenkinsV, FallowfieldL, & SaulJ. (2001). Information needs of patients with cancer: results from a large study in UK cancer centres. British Journal of Cancer. JinO., LiuN.N., ZhaoK., YuY., & YangQ. (2011). Transferring topical knowledge from auxiliary long texts for short text clustering. Proceedings of the 20th ACM international conference on information and knowledge management. JohnstonC.Allen. (2013). Online health communities: An assessment of the influence of participation on patient empowerment outcomes. Information Technology & People. Jonathan ChangBoyd-Graber, Sean Gerrish, Chong Wang, David M. BleiJordan. (2009). Reading Tea Leaves: How Humans Interpret Topic Models. Advances in Neural Information Processing Systems 22 (NIPS 2009). KrestelR., FankhauserP., & NejdlW. (2009). Latent Dirichlet allocation for tag recommendation. Proceedings of the third ACM conference on recommender systems. L.AntheunisaMarjolijn, TatesaKiek, & E.NieboerbTheodoor. (2013). Patients’ and health professionals’ use of social media in health care: Motives, barriers and expectations. Patient Education and Counseling. L.BenderJacqueline, JoelKatze, E.FerrisLorraine, & R.JadadAlejandro. (2013). A multi-method study of the use of breast cancer online communities. Patient Education and Counseling. LauY. S.Annie, & KwokM. Y.Trevor. (2009). Social Features in Online Communities for Healthcare Consumers – A Review. Online Communities and Social Computing . LiuNa, TongYu, & ChanChuanHock. (2017). Information Seeking in Online Healthcare Communities: The Dual Influence From Social Self and Personal Self. IEEE transactions on engineering management. MaskeriGirish, SarkarSantonu, & HeafieldKenneth. (2008). Mining Business Topics in Source Code using Latent. ISEC '08 Proceedings of the 1st India software engineering conference. MoK. H.Phoenix, & S.Neil. (2008). Exploring the Communication of Social Support within Virtual Communities: A Content Analysis of Messages Posted to an Online HIV/AIDS Support Group. CyberPsychology & Behavior. NambisanPriya. (2011). Information seeking and social support in online health communities: impact on patients' perceived empathy . Journal of the American Medical Informatics Association. NzaliDonald TapiMike. (2017). What Patients Can Tell Us: Topic Analysis for Social Media on Breast Cancer. JMIR Medical Informatics. ParkJungsik, & RyuUkYoung. (2014). Online Discourse on Fibromyalgia: Text-Mining to Identify Clinical Distinction and Patient Concerns. Medical Science Monitor. PartalaT. (2011). sychological needs and virtual worlds: Case Second Life. International Journal of Human-Computer Studies. PaulJ.Michael, & DredzeMark. (2014). Discovering Health Topics in Social Media Using Topic Models. PLoS ONE . Romain DeveaudSanjuan, Patrice BellotEric. (2014). Accurate and Effective Latent Concept Modeling for Ad Hoc Information Retrieval. Revue des Sciences et Technologies de l’Information - Série Document. TangXuning, & YangC.Christopher. (2010 ). Identifing influential users in an online healthcare social network. IEEE International Conference on Intelligence and Security Informatics . TangXuning, & YangC.Christopher. (2010). Identifing influential users in an online healthcare social network. IEEE International Conference on Intelligence and Security Informatics. Thomas L. GriffithsSteyversMark. (2004). Finding scientific topics. Proceedings of the National Academy of Sciences. TsaiChih-Hao. (1996). MMSEG: A Word Identification System for Mandarin Chinese Text Based on Two Variants of the Maximum Matching Algorithm. WangShiliang, PaulJ.Michael, & DredzeMark. (2014). Exploring Health Topics in Chinese Social Media: An Analysis of Sina Weibo. AAAI Publications, Workshops at the Twenty-Eighth AAAI Conference on Artificial Intelligence. Weizhong ZhaoJ Chen, Roger Perkins, Zhichao Liu, Weigong Ge, Yijun Ding and Wen ZouJames. (2015). A heuristic approach to determine an appropriate number of topics in topic modeling. BMC Bioinformatics. WicksPaul, VaughanETimothy, MassagliPMichael, & HeywoodJames. (2011). Accelerated clinical discovery using self-reported patient data collected online and a patient-matching algorithm. Nature Biotechnology . YinJianhua, & WangJianyong. (2014). A Dirichlet Multinomial Mixture Model-based Approach for Short Text Clustering. KDD '14 Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining. ZhangXing, LiuShan, DengZhaohua, & ChendXing. (2017). Knowledge sharing motivations in online health communities: A comparative study of health professionals and normal users. Computers in Human Behavior. | |
dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/71568 | - |
dc.description.abstract | 多年來,癌症高居台灣十大死因之首。有效的資訊可以緩解癌症病患的焦慮感,故癌症病患有其資訊需求。由於實體資源往往無法提供足夠資訊,癌症病患轉而使用線上資源以滿足他們的資訊需求。近年來,癌症病患漸漸增加對線上癌症病友社群的使用。
本研究的目標為定義出線上癌症病友社群所討論的主題。因應病患使用線上癌症病友社群的趨勢,有效的分析社群貼文、研究病患所討論的內容,能讓研究人員更了解患者,並提供更好的服務。 本文研究所使用的素材為病患在兩組線上癌症病友社群的貼文。由於社群媒體資料為非結構性資訊,本研究選擇主題模型作為主要的研究方法。在搜尋相關文獻後,本研究選定LDA主題模型對此二文本進行分析。 本研究成功擷取病患在兩個不同社群中所討論的主題,包含臉書社群中的六個主題 ”副作用”、”支持”、”治療”、”新藥”、”問題”``、”祝福”以及批踢踢抗癌版中的五個主題”照護”、”手術”、”病友團體”、”病患狀況”、”藥物”。 本研究證實主題模型能協助研究員定義病患需求,從大量文本裡擷取出隱含的文義。 | zh_TW |
dc.description.abstract | Background: Cancer patients utilize online information to fulfill their information need. Cancer patients and their relatives increasingly participate in online disease communities. These platforms allow cancer patients to exchange their experience of dealing with the disease by posting open-end discussions.
Objective: The aim of this research is to automatically identify different cancer-related topics discussed on online cancer patient community. We use text-mining techniques to examine what patients say about their experiences during cancer journey and to address their unmet needs. By analyzing these user-generated contents, we can get a better understanding of how people participate in the online discussions. Methods: We applied LDA models on the two datasets collected from the Facebook group “Anti-Cancer Alliance” and the “Anti-Cancer” board in PTT, a well-known BBS forum in Taiwan. We conducted some relevant data preprocessing, then we applied LDA model to more than 10,000 discussions to study what cancer patients say online about their disease journey. Results: Experiment results demonstrate that health-related hot topics primarily include 6 topics from posts of Facebook users, which are “Side effect”, “Support”, “Treatment”, “New drug”, “ Questions” and “Blessing”; and 5 topic from BBS users, which are “Care”, “Surgery”, “Support group”, “Patient condition” and” Medicine”. Conclusions: This research shows the potential for extracting keywords to confirm the clinical distinction, and text-mining can help objectively understand the concerns of patients by generalizing their large number of subjective illness experiences. | en |
dc.description.provenance | Made available in DSpace on 2021-06-17T06:03:30Z (GMT). No. of bitstreams: 1 ntu-108-R05741051-1.pdf: 1361560 bytes, checksum: ebbf84b2af0ac7e1033f2cf43fecbd75 (MD5) Previous issue date: 2019 | en |
dc.description.tableofcontents | 口試委員會審定書 #
中文摘要 i ABSTRACT ii CONTENTS iii LIST OF FIGURES vi LIST OF TABLES vii Chapter 1 Introduction 1 1.1 Background 1 1.2 Objective 2 1.3 Method and Process 3 Chapter 2 Literature Review 5 2.1 Online Health Communities 5 2.1.1 Application of Social Media in Healthcare 5 2.1.2 Online Healthcare Communities 7 2.1.3 Patient Information Sharing/ Seeking Behavior 8 2.2 Related research 9 2.2.1 Identifying patient needs 9 2.2.2 Applied Topic Models to solve health-related issues 10 2.2.3 Applied Topic Models to Identify Patient Needs 10 Chapter 3 Research Method 12 3.1 Research Process 12 3.2 Latent Dirichlet Allocation 12 3.2.1 Topic Models 12 3.2.2 Latent Dirichlet Allocation 13 3.2.3 LDA Algorithm 14 Chapter 4 Result 16 4.1 Data Collection 16 4.1.1 Data Collecting Technique 16 4.1.2 Anti-Cancer Alliance 17 4.1.3 PTT Anti-Cancer Board 18 4.2 Data preprocessing 19 4.2.1 Basic Data Cleansing 20 4.2.2 Tokenization 21 4.2.3 Feature Extraction 24 4.3 Basic Analysis 26 4.3.1 Descriptive Statistics 26 4.3.2 Disease Stage 28 4.3.3 Term Frequency 28 4.4 Parameter Setting 29 4.4.1 Topic Number 29 4.4.2 Distribution Parameter 32 4.5 Result of Topic Models 33 Chapter 5 Conclusion and Suggestion 36 5.1 Conclusion 36 5.1.1 The Applicability of LDA Modeling in Related Propose 36 5.1.2 The Result of LDA Modeling 36 5.2 Discussion 38 5.2.1 Limitation 38 5.2.2 Future Research 39 Reference 41 | |
dc.language.iso | en | |
dc.title | 以LDA主題模型探討線上醫療社群之癌症病人需求 | zh_TW |
dc.title | An exploration on cancer patients needs by LDA topic modeling in online health community | en |
dc.type | Thesis | |
dc.date.schoolyear | 107-1 | |
dc.description.degree | 碩士 | |
dc.contributor.oralexamcommittee | 孔令傑,盧信銘 | |
dc.subject.keyword | 主題模型,LDA,健康需求探勘,非監督式學習,癌症, | zh_TW |
dc.subject.keyword | Topic models,LDA,Healthcare needs,Unsupervised learning,Cancer, | en |
dc.relation.page | 46 | |
dc.identifier.doi | 10.6342/NTU201900229 | |
dc.rights.note | 有償授權 | |
dc.date.accepted | 2019-01-28 | |
dc.contributor.author-college | 管理學院 | zh_TW |
dc.contributor.author-dept | 商學研究所 | zh_TW |
顯示於系所單位: | 商學研究所 |
文件中的檔案:
檔案 | 大小 | 格式 | |
---|---|---|---|
ntu-108-1.pdf 目前未授權公開取用 | 1.33 MB | Adobe PDF |
系統中的文件,除了特別指名其著作權條款之外,均受到著作權保護,並且保留所有的權利。