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    Please use this identifier to cite or link to this item: http://ir.lib.ncu.edu.tw/handle/987654321/72138


    Title: 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
    Authors: 李佳桓;Li,Jia-Huan
    Contributors: 資訊管理學系
    Keywords: 共詞分析;合著分析;社會網路分析;跨領域影響力指標;Co-word analysis;Co-author analysis;Social network analysis;Subject area impact factor
    Date: 2016-07-18
    Issue Date: 2016-10-13 14:27:50 (UTC+8)
    Publisher: 國立中央大學
    Abstract: 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。
    ;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.
    Appears in Collections:[資訊管理研究所] 博碩士論文

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