Dongxiao Gu1, Jingjing Li2, Xingguo Li3, Changyong Liang4. 1. School of Management, Hefei University of Technology, 193 Tunxi Road, Hefei, Anhui 230009, China. Electronic address: gudongxiao@hfut.edu.cn. 2. School of Management, Hefei University of Technology, 193 Tunxi Road, Hefei, Anhui 230009, China. Electronic address: 847840274@qq.com. 3. School of Management, Hefei University of Technology, 193 Tunxi Road, Hefei, Anhui 230009, China. Electronic address: mikehfut0551@163.com. 4. School of Management, Hefei University of Technology, 193 Tunxi Road, Hefei, Anhui 230009, China; National Joint Engineering Research Center for Intelligent Decision and Information Systems, 193 Tunxi Road, Hefei, Anhui 230009, China. Electronic address: cyliang@hfut.edu.cn.
Abstract
BACKGROUND: In recent years, the literature associated with healthcare big data has grown rapidly, but few studies have used bibliometrics and a visualization approach to conduct deep mining and reveal a panorama of the healthcare big data field. METHODS: To explore the foundational knowledge and research hotspots of big data research in the field of healthcare informatics, this study conducted a series of bibliometric analyses on the related literature, including papers' production trends in the field and the trend of each paper's co-author number, the distribution of core institutions and countries, the core literature distribution, the related information of prolific authors and innovation paths in the field, a keyword co-occurrence analysis, and research hotspots and trends for the future. RESULTS: By conducting a literature content analysis and structure analysis, we found the following: (a) In the early stage, researchers from the United States, the People's Republic of China, the United Kingdom, and Germany made the most contributions to the literature associated with healthcare big data research and the innovation path in this field. (b) The innovation path in healthcare big data consists of three stages: the disease early detection, diagnosis, treatment, and prognosis phase, the life and health promotion phase, and the nursing phase. (c) Research hotspots are mainly concentrated in three dimensions: the disease dimension (e.g., epidemiology, breast cancer, obesity, and diabetes), the technical dimension (e.g., data mining and machine learning), and the health service dimension (e.g., customized service and elderly nursing). CONCLUSION: This study will provide scholars in the healthcare informatics community with panoramic knowledge of healthcare big data research, as well as research hotspots and future research directions.
BACKGROUND: In recent years, the literature associated with healthcare big data has grown rapidly, but few studies have used bibliometrics and a visualization approach to conduct deep mining and reveal a panorama of the healthcare big data field. METHODS: To explore the foundational knowledge and research hotspots of big data research in the field of healthcare informatics, this study conducted a series of bibliometric analyses on the related literature, including papers' production trends in the field and the trend of each paper's co-author number, the distribution of core institutions and countries, the core literature distribution, the related information of prolific authors and innovation paths in the field, a keyword co-occurrence analysis, and research hotspots and trends for the future. RESULTS: By conducting a literature content analysis and structure analysis, we found the following: (a) In the early stage, researchers from the United States, the People's Republic of China, the United Kingdom, and Germany made the most contributions to the literature associated with healthcare big data research and the innovation path in this field. (b) The innovation path in healthcare big data consists of three stages: the disease early detection, diagnosis, treatment, and prognosis phase, the life and health promotion phase, and the nursing phase. (c) Research hotspots are mainly concentrated in three dimensions: the disease dimension (e.g., epidemiology, breast cancer, obesity, and diabetes), the technical dimension (e.g., data mining and machine learning), and the health service dimension (e.g., customized service and elderly nursing). CONCLUSION: This study will provide scholars in the healthcare informatics community with panoramic knowledge of healthcare big data research, as well as research hotspots and future research directions.
Authors: Ho Heon Kim; Bora Kim; Segyeong Joo; Soo-Yong Shin; Hyo Soung Cha; Yu Rang Park Journal: J Med Internet Res Date: 2019-08-06 Impact factor: 5.428