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检测医疗保健领域数字孪生的潜在主题和趋势：基于结构主题模型的系统综述。

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

作者信息

Sheng Bo, Wang Zheyu, Qiao Yujiao, Xie Sheng Quan, Tao Jing, Duan Chaoqun

机构信息

School of Mechatronic Engineering and Automation, Shanghai University, Shanghai, China.

Shanghai Key Laboratory of Intelligent Manufacturing and Robotics, Shanghai University, Shanghai, China.

出版信息

Digit Health. 2023 Oct 12;9:20552076231203672. doi: 10.1177/20552076231203672. eCollection 2023 Jan-Dec.

DOI:10.1177/20552076231203672
PMID:37846404
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC10576938/
Abstract

OBJECTIVE

Digital 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.

METHODS

A 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.

RESULTS

A 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."

CONCLUSIONS

This 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.

摘要

目的

数字孪生（DT）最近受到了广泛关注，为未来医疗保健提供了新的思路和可能性。本综述旨在进行定量分析，以剖析DT在医疗保健领域的具体研究内容、研究重点和趋势。同时，本综述打算将“医疗保健”的内涵扩展到“疾病治疗”和“健康提升”两个方向，以便全面分析“DT + 医疗保健”领域的内容。

方法

由于其主题分析能力和通用性，一种名为结构主题建模（STM）的数据挖掘方法被用作分析工具。谷歌学术、科学网和中国知网为本综述提供了材料论文。

结果

共收集了2018年至2022年间发表的94篇高质量论文，并将其分为八个主题，共同涵盖了医疗保健领域更广泛范围内的变革性影响。出现了三个主要发现：（1）医疗保健领域发表的论文主要集中在技术开发（人工智能、物联网等）和应用场景（个性化、精准和实时健康服务）；（2）研究主题的受欢迎程度受到多种因素的影响，包括政策、新冠疫情和新兴技术；（3）对主题的偏好各不相同，总体倾向于“健康提升”属性。

结论

本综述强调了实时能力和准确性在塑造DT未来方面的重要性，其中算法和数据传输方法对于实现这些目标至关重要。此外，组学和元宇宙等技术进步为DT在医疗保健领域开辟了新的可能性。这些发现为现有文献做出了贡献，通过提供定量见解和有价值的指导，使研究人员能够领先于时代潮流。

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