Detecting latent topics and trends of digital twins in healthcare: A structural topic model-based systematic review

被引：8

作者：

Sheng, Bo ^{[1
,2
]}

Wang, Zheyu ^{[1
]}

Qiao, Yujiao ^{[3
]}

Xie, Sheng Quan ^{[4
]}

Tao, Jing ^{[1
,5
]}

Duan, Chaoqun ^{[1
,5
]}

机构：

[1] Shanghai Univ, Sch Mechatron Engn & Automat, Shanghai, Peoples R China

[2] Shanghai Univ, Shanghai Key Lab Intelligent Mfg & Robot, Shanghai, Peoples R China

[3] ShanghaiTech Univ, Ctr Innovat Teaching & Learning, Shanghai, Peoples R China

[4] Univ Leeds, Sch Elect & Elect Engn, Leeds, England

[5] Shanghai Univ, Sch Mechatron Engn & Automat, Shanghai 200444, Peoples R China

来源：

DIGITAL HEALTH | 2023年 / 9卷

关键词：

Healthcare; digital twin; structure topic modeling; artificial intelligence; text data mining;

D O I：

10.1177/20552076231203672

中图分类号：

R19 [保健组织与事业（卫生事业管理）];

学科分类号：

摘要：

ObjectiveDigital twins (DTs) have received widespread attention recently, providing new ideas and possibilities for future healthcare. This review aims to provide a quantitative review to analyze specific study contents, research focus, and trends of DT in healthcare. Simultaneously, this review intends to expand the connotation of "healthcare" into two directions, namely "Disease treatment" and "Health enhancement" to analyze the content within the "DT + healthcare" field thoroughly.MethodsA data mining method named Structure Topic Modeling (STM) was used as the analytical tool due to its topic analysis ability and versatility. Google Scholar, Web of Science, and China National Knowledge Infrastructure supplied the material papers in this review.ResultsA total of 94 high-quality papers published between 2018 and 2022 were gathered and categorized into eight topics, collectively covering the transformative impact across a broader spectrum in healthcare. Three main findings have emerged: (1) papers published in healthcare predominantly concentrate on technology development (artificial intelligence, Internet of Things, etc.) and application scenarios(personalized, precise, and real-time health service); (2) the popularity of research topics is influenced by various factors, including policies, COVID-19, and emerging technologies; and (3) the preference for topics is diverse, with a general inclination toward the attribute of "Health enhancement."ConclusionsThis review underscores the significance of real-time capability and accuracy in shaping the future of DT, where algorithms and data transmission methods assume central importance in achieving these goals. Moreover, technological advancements, such as omics and Metaverse, have opened up new possibilities for DT in healthcare. These findings contribute to the existing literature by offering quantitative insights and valuable guidance to keep researchers ahead of the curve.

引用

页数：27