Enhancing emerging technology discovery in nanomedicine by integrating innovative sentences using BERT and NLDA  

作　　者：Yifan Wang Xiaoping Liu Xiang-Li Zhu 

机构地区：[1]National Science Library,Chinese Academy of Sciences,Beijing 100190,P.R.China [2]Department of Information Resources Management,School of Economics and Management,University of Chinese Academy of Sciences,Beijing,100190,P.R.China

出　　处：《Journal of Data and Information Science》2024年第4期155-195,共41页数据与情报科学学报（英文版）

基　　金：supported by the National Natural Science Foundation of China(Project No.22342011).

摘　　要：Purpose:Nanomedicine has significant potential to revolutionize biomedicine and healthcare through innovations in diagnostics,therapeutics,and regenerative medicine.This study aims to develop a novel framework that integrates advanced natural language processing,noise-free topic modeling,and multidimensional bibliometrics to systematically identify emerging nanomedicine technology topics from scientific literature.Design/methodology/approach:The framework involves collecting full-text articles from PubMed Central and nanomedicine-related metrics from the Web of Science for the period 2013-2023.A fine-tuned BERT model is employed to extract key informative sentences.Noiseless Latent Dirichlet Allocation(NLDA)is applied to model interpretable topics from the cleaned corpus.Additionally,we develop and apply metrics for novelty,innovation,growth,impact,and intensity to quantify the emergence of novel technological topics.Findings:By applying this methodology to nanomedical publications,we identify an increasing emphasis on research aligned with global health priorities,particularly inflammation and biomaterial interactions in disease research.This methodology provides deeper insights through full-text analysis and leading to a more robust discovery of emerging technologies.Research limitations:One limitation of this study is its reliance on the existing scientific literature,which may introduce publication biases and language constraints.Additionally,manual annotation of the dataset,while thorough,is subject to subjectivity and can be time-consuming.Future research could address these limitations by incorporating more diverse data sources,and automating the annotation process.Practical implications:The methodology presented can be adapted to explore emerging technologies in other scientific domains.It allows for tailored assessment criteria based on specific contexts and objectives,enabling more precise analysis and decision-making in various fields.Originality/value:This study offers a comprehensive framework for id

关 键 词：BIBLIOMETRICS NANOMEDICINE Emerging technologies BERT Topic modeling 

分 类 号：TP391.1[自动化与计算机技术—计算机应用技术] R-05[自动化与计算机技术—计算机科学与技术]