博碩士論文 103423017 詳細資訊




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姓名 李佳桓(Jia-Huan Li)  查詢紙本館藏   畢業系所 資訊管理學系
論文名稱 以社會網路分析觀點探討巨量資料在健康保健領域之研究發展
(Social network analysis: Research pattern of big data in healthcare)
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檔案 [Endnote RIS 格式]    [Bibtex 格式]    [相關文章]   [文章引用]   [完整記錄]   [館藏目錄]   至系統瀏覽論文 (2021-6-30以後開放)
摘要(中) 隨著健康保健資料電子化,相關資料量大幅成長,許多證據顯示健康保健巨量資料已成為一門廣受世界各國關注的科學領域,然而目前卻還沒有一個確鑿的資訊計量分析(Informetric Analysis)來幫助研究者快速掌握此領域的發展狀況,並促進該領域的發展。本研究以社會網路分析觀點探討健康保健巨量資料研究領域的整體研究趨勢,並辨識此領域最有影響力的學者、機構與國家。
本研究蒐集Scopus學術資料庫最近20年健康保健巨量資料相關期刊論文,並採用共詞分析(Co-word analysis)與合著分析(Co-author analysis)技術建構健康保健巨量資料領域的文獻知識地圖以及合著網路地圖,來辨識健康保健巨量資料領域研究趨勢並找出最有影響力的重要學者與機構,最後本研究也透過發展一新指標來計算此領域重要學者的跨領域影響力。
本研究結果顯示大多數健康保健巨量資料研究聚焦於少數關鍵字,大多數關鍵字僅出現於少數的研究當中,例如出現一次的關鍵字佔了全部關鍵字80.9%,意味著此領域多為單一個案研究,欠缺完整研究體系。本研究詳細地描繪健康保健巨量資料研究整體研究發展情形,幫助研究者找出此領域的研究缺口與趨勢,並找出適合的研究合作對象,而政府與相關機構也可透過本研究結果進行資源分配,投注績效、影響力較大的學者與機構更多的資源。
摘要(英) Increasing evidence shows that the application of big data in healthcare has become an important research area. However, no previous study has undertaken a comprehensive Informetric analysis in the field. To fill this knowledge gap, this study examined the research patterns and trends of big data in healthcare from the perspective of social network analysis. The relevant data were collected for the last 20 years from the Scopus database. This study used co-word analysis and co-author analysis to reveal patterns of research in healthcare big data and to identify the most influential author, institution and country in this field. In addition, we also evaluated the inter-subject area influence of authors by the new index (Subject area impact factor).
The results of analysis indicate that the research structure of big data in healthcare is a scale-free network; that is, most academic attention focused on few keywords; about 80.9% of all the keywords were only received little attention. The keyword frequency appears to obey power-law distribution. The research patterns of healthcare big data revealed in this study can help researchers identify critical research gaps and find proper research collaborators. Government and relative institutions may allocate more resources to efficient authors and institutions base on our results.
關鍵字(中) ★ 共詞分析
★ 合著分析
★ 社會網路分析
★ 跨領域影響力指標
關鍵字(英) ★ Co-word analysis
★ Co-author analysis
★ Social network analysis
★ Subject area impact factor
論文目次 中文摘要 i
英文摘要 ii
致謝辭 iii
目錄 iv
圖目錄 vii
表目錄 viii
一、 緒論 1
1-1 研究背景與動機 1
1-2 研究目的 3
1-3 研究方法 4
1-4 研究重要性 4
1-5 論文架構 5
二、文獻探討 6
2-1 健康保健巨量資料領域相關研究 6
2-2 共詞分析(Co-word analysis) 8
2-3 合著分析(Co-author analysis) 9
2-4 小結 12
三、 研究方法 12
3-1 資料蒐集(Data collection) 13
3-2 資料前處理(Data pre-processing) 13
3-3-1 共詞分析(Co-word Analysis) 14
3-3-2 合著分析(Co-author analysis) 15
3-4 視覺化(Visualization) 16
四、實驗結果與討論 17
4-1 文獻數量變化趨勢 17
4-2 關鍵字頻率分析 18
4-2 中心性分析 21
4-3 視覺化分析 23
4-4 統計理論與方法 27
4-5 合著率分析 28
4-6 文獻產量分析(作者) 30
4-7 中心性分析(作者) 32
4-8 作者文獻影響力分析 35
4-9 視覺化分析(作者) 42
4-10 文獻產量分析(機構) 44
4-11 中心性分析(機構) 45
4-12 視覺化分析(機構) 48
4-13 文獻產量分析(國家) 50
4-14 中心性分析(國家) 50
4-15 視覺化分析(國家) 52
五、結論與建議 54
5-1 研究結論 54
5-1-1 文獻數量變化 54
5-1-2 共詞分析之結果 54
5-1-3 合著分析之結果 55
5-2 研究限制與未來建議 58
5-2-1 研究限制 58
5-2-2 共詞分析結果之建議 59
5-2-3 合著分析結果之建議 59
5-2-4 資訊計量研究之未來應用 60
參考文獻 61
附錄一 Scopus學科分類表 65
附錄二 關鍵字頻率表 70
附錄三 關鍵字程度中心性表 73
附錄四 關鍵字中介中心性表 76
附錄五 理論與方法表 79
附錄六 作者文獻產量表 82
附錄七 作者程度中心性表(只包含高產出學者) 84
附錄八 作者程度中心性表 86
附錄九 作者中介中心性表 88
附錄十 機構文獻產量表 90
附錄十一 機構程度中心性表 92
附錄十二 機構中介中心性表 94
附錄十三 國家文獻產量表 96
附錄十四 國家程度中心性表 98
附錄十五 國家中介中心性表 100
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指導教授 許文錦(Wen-Chin Hsu) 審核日期 2016-7-18
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