Lim Yooseok, Kim Eun Man
Department of Industrial and Information Systems Engineering, Soongsil University.
Department of Nursing Science, SunMoon University.
Stud Health Technol Inform. 2025 Aug 7;329:1852-1853. doi: 10.3233/SHTI251247.
This study identify the featured topics of studies related to digital twin (DT) and healthcare through Structural Topic Modeling (STM). Papers on the DT in healthcare were gathered from five databases between 2018 and 2024. An STM model was developed, and the best topics were selected based on semantic coherence, lower bound, and held-out likelihood. Eight topics were emerged from the literature. The integration of advanced technologies, led by DT, is revolutionizing healthcare and manufacturing by delivering more efficient, personalized, and innovative solutions.
本研究通过结构主题模型（STM）确定了与数字孪生（DT）和医疗保健相关的研究主题。2018年至2024年间，从五个数据库收集了关于医疗保健领域数字孪生的论文。开发了一个STM模型，并根据语义连贯性、下限和保留似然性选择了最佳主题。文献中出现了八个主题。以数字孪生为首的先进技术的整合，正在通过提供更高效、个性化和创新的解决方案，彻底改变医疗保健和制造业。
