人工智慧運用在醫學領域研究之網路與主題分析;Network and Topic Analysis of the Artificial Intelligence Research in Medicine

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Title:	人工智慧運用在醫學領域研究之網路與主題分析;Network and Topic Analysis of the Artificial Intelligence Research in Medicine
Authors:	許景鑫;Hsu, Chin-Hsing
Contributors:	企業管理學系
Keywords:	網路分析;主題分析;書目耦合;人工智慧;共同引用
Date:	2020-07-17
Issue Date:	2020-09-02 15:32:40 (UTC+8)
Publisher:	國立中央大學
Abstract:	儘管已經廣泛研究了人工智慧（Artificial intelligence, A I）在醫學中的應用，但仍需進行其他相關的全面文獻綜述。這項研究的目的是整合網絡分析和主題分析，以分析醫學領域有關AI的研究發展趨勢。通過使用基於關鍵字的搜尋，從Scopus數據庫中收集了1963–2018年間發表的所有相關文章，以及它們的作者姓名，通訊地址，文章標題，出版年份，關鍵字，引用計數，主題，期刊名稱以及出版物和出版商信息。在這項研究中進行的網絡分析是基於共現，共引和書目耦合分析，並經由網絡可視化顯示。通過確定關鍵字，作者和期刊的結構和它們之間的關係，進行了聚類分析，並利用關聯的強度評估了文獻中的趨勢並確定了研究重點。這項研究還使用主題建模來根據文檔文本發現文檔集中的隱藏主題結構，並揭示文檔集中所有可能的主題以及每個文檔的主題比例。首先，在共現和作者關鍵詞聚類分析中，結果揭示了關聯和方法的發展方向。其次，關於被引論文的作者網絡分析，結果表明相關研究領域的共性可以作為該領域未來研究者的參考學習合作。關於被引文獻資源網絡，結果表明不同的期刊具有相同的特徵並被共同引用，具有指導該領域整體研究的特徵和進展。在書目耦合引用來源網絡中，結果揭示了AI在醫學領域研究的出版趨勢。最後，本研究採用主題建模進行主題分析，結果揭示了6個主要主題的方法和研究領域的趨勢，可以為將來的研究提供重要參考。;Although the application of artificial intelligence (AI) in medicine has been researched extensively, additional relevant comprehensive literature reviews are warranted. The objective of this study is to integrate network analysis and topic analysis to analyze the development trends in research regarding AI in the medicine domain. By using keyword-based search, all relevant articles published over 1963–2018 were collected from the Scopus database, and their author names, correspondence addresses, article titles, publishing years, keywords, citation count, themes, journal names, and publication and publisher information were extracted. The network analysis conducted in this study was based on co-occurrence, co-citation, and bibliographic coupling analysis and was demonstrated through network visualization. By determining the structure of and relationships between keywords, authors, and journal, cluster analysis was conducted, and the strength of associations was employed to evaluate the trend in the literature and define the research focal points. This study also employed topic modeling to discover hidden topic structures in document sets on the basis of the texts of the documents and to reveal all possible topics in a document set as well as the proportions of the topics of each document. First, In the co-occurrence and author keyword cluster analysis, the results revealed the development direction of association and methods. Second, regarding the co-citation cited authors network analysis, the results revealed the commonality of related research areas can be used as a reference learning cooperation for future researchers in this field. With respect to the co-citation cited sources network, the results revealed different journals have the same characteristics and are co-cited, which has the characteristics and progress of guiding the overall research in this field. In the bibliographic coupling cited sources network, the result revealed the publication trend of studies in the field of AI in medicine. Finally, this study employed topic modeling for topic analysis, the results revealed the methods and research field trends on 6 major topics can provide important references may serve for future research.
Appears in Collections:	[Graduate Institute of Business Administration] Electronic Thesis & Dissertation

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