scholarly journals Study on Horizon Scanning with a Focus on the Development of AI-Based Medical Products: Citation Network Analysis

2021 ◽  
Author(s):  
Takuya Takata ◽  
Hajime Sasaki ◽  
Hiroko Yamano ◽  
Masashi Honma ◽  
Mayumi Shikano
Keyword(s):  
Network Analysis ◽  
Text Mining ◽  
Citation Network ◽  
Research Progress ◽  
Research Trend ◽  
Medical Products ◽  
Medical Technologies ◽  
Horizon Scanning ◽  
Citation Network Analysis ◽  
Clinical Records

AbstractHorizon scanning for innovative technologies that might be applied to medical products and requires new assessment approaches to prepare regulators, allowing earlier access to the product for patients and an improved benefit/risk ratio. The purpose of this study is to confirm that citation network analysis and text mining for bibliographic information analysis can be used for horizon scanning of the rapidly developing field of AI-based medical technologies and extract the latest research trend information from the field. We classified 119,553 publications obtained from SCI constructed with the keywords “conventional,” “machine-learning,” or “deep-learning" and grouped them into 36 clusters, which demonstrated the academic landscape of AI applications. We also confirmed that one or two close clusters included the key articles on AI-based medical image analysis, suggesting that clusters specific to the technology were appropriately formed. Significant research progress could be detected as a quick increase in constituent papers and the number of citations of hub papers in the cluster. Then we tracked recent research trends by re-analyzing “young” clusters based on the average publication year of the constituent papers of each cluster. The latest topics in AI-based medical technologies include electrocardiograms and electroencephalograms (ECG/EEG), human activity recognition, natural language processing of clinical records, and drug discovery. We could detect rapid increase in research activity of AI-based ECG/EEG a few years prior to the issuance of the draft guidance by US-FDA. Our study showed that a citation network analysis and text mining of scientific papers can be a useful objective tool for horizon scanning of rapidly developing AI-based medical technologies.

scholarly journals Study on horizon-scanning with a focus on the development of AI-based medical products: citation network analysis

2021 ◽  
Author(s):  
Takuya Takata ◽  
Hajime Sasaki ◽  
Hiroko Yamano ◽  
Masashi Honma ◽  
Mayumi Shikano
Keyword(s):  
Network Analysis ◽  
Medical Image ◽  
Medical Image Analysis ◽  
Citation Network ◽  
Research Progress ◽  
Research Papers ◽  
Medical Products ◽  
Horizon Scanning ◽  
Significant Research ◽  
Citation Network Analysis

ABSTRACTObjectivesHorizon-scanning for innovative technologies that might be applied to medical products and require new assessment approaches/regulations will help to prepare regulators, allowing earlier access to the product for patients and an improved benefit/risk ratio. In this study, we focused on the field of AI-based medical image analysis as a retrospective example of medical devices, where many products have recently been developed and applied. We proposed and validated horizon-scanning using citation network analysis and text mining for bibliographic information analysis.Methods and analysisResearch papers for citation network analysis which contain “convolutional*” OR “machine-learning” OR “deep-learning” were obtained from Science Citation Index Expanded (SCI-expanded) in the Web of Science (WoS). The citation network among those papers was converted into an unweighted network with papers as nodes and citation relationships as links. The network was then divided into clusters using the topological clustering method and the characteristics of each cluster were confirmed by extracting a summary of frequently cited academic papers, and the characteristic keywords, in the cluster.ResultsWe classified 119,553 publications obtained from SCI and grouped them into 36 clusters. Hence, it was possible to understand the academic landscape of AI applications. The key articles on AI-based medical image analysis were included in one or two clusters, suggesting that clusters specific to the technology were appropriately formed. Based on the average publication year of the constituent papers of each cluster, we tracked recent research trends. It was also suggested that significant research progress would be detected as a quick increase in constituent papers and the number of citations of hub papers in the cluster.ConclusionWe validated that citation network analysis applies to the horizon-scanning of innovative medical devices and demonstrated that AI-based electrocardiograms and electroencephalograms can lead to the development of innovative products.Article SummaryStrengths and limitations of this studyCitation network analysis can provide an academic landscape in the investigated research field, based on the citation relationship of research papers and objective information, such as characteristic keywords and publication year.It might be possible to detect possible significant research progress and the emergence of new research areas through analysis every several months.It is important to confirm the opinions of experts in this area when evaluating the results of the analysis.Information on patents and clinical trials for this analysis is currently unavailable.

scholarly journals Study on Horizon Scanning by Citation Network Analysis and Text Mining: A Focus on Drug Development Related to T Cell Immune Response

2021 ◽  
Author(s):  
Erika Fujii ◽  
Takuya Takata ◽  
Hiroko Yamano ◽  
Masashi Honma ◽  
Masafumi Shimokawa ◽  
...  
Keyword(s):  
Network Analysis ◽  
Text Mining ◽  
Product Development ◽  
Immune Checkpoint Inhibitors ◽  
Checkpoint Inhibitors ◽  
Citation Network ◽  
Research Trends ◽  
Horizon Scanning ◽  
Scientific Papers ◽  
Citation Network Analysis

AbstractCertain innovative technologies applied to medical product development require novel evaluation approaches and/or regulations. Horizon scanning for such technologies will help regulators prepare, allowing earlier access to the product for patients and an improved benefit/risk ratio. This study investigates whether citation network analysis and text mining of scientific papers could be a tool for horizon scanning in the field of immunology, which has developed over a long period, and attempts to grasp the latest research trends. As the result of the analysis, the academic landscape of the immunology field was identified by classifying 90,450 papers (obtained from PubMED) containing the keyword “immune* and t lymph*” into 38 clusters. The clustering was indicative of the research landscape of the immunology field. To confirm this, immune checkpoint inhibitors were used as a retrospective test topic of therapeutics with new mechanisms of action. Retrospective clustering around immune checkpoint inhibitors was found, supporting this approach. The analysis of the research trends over the last 3 to 5 years in this field revealed several candidate topics, including ARID1A gene mutation, CD300e, and tissue resident memory T cells, which shows notable progress and should be monitored for future possible product development. Our results have demonstrated the possibility that citation network analysis and text mining of scientific papers can be a useful objective tool for horizon scanning of life science fields such as immunology.

scholarly journals Text Mining and Citation Network of Ecosystem Services Publications

2019 ◽  
Author(s):  
Muhammad Malik Ar-Rahiem
Keyword(s):  
Ecosystem Services ◽  
Network Analysis ◽  
Text Mining ◽  
Biodiversity Conservation ◽  
Bibliometric Analysis ◽  
Natural Capital ◽  
Citation Network ◽  
Well Being ◽  
Bibliographic Coupling ◽  
Citation Network Analysis

Ecosystem Services is an important concept to achieve Sustainable Development Goals 2030. For the past 20 years, this concept has grown exponentially and the metadata of these publications can be considered as big data. A bibliometric analysis was conducted to Ecosystem Services publications from Web of Science database, which are text-mining analysis, bibliographic coupling, and citation network analysis. Text-mining analysis results were a cluster map of keywords representing the content of abstract and title from 4203 publications in the dataset. Bibliographic coupling analysis results were a cluster of documents which analyzed using natural language processing to extract the main idea of the documents. Using these two analysis insight about ecosystem services are obtained. Ecosystem services in general can be divided into 6 big clusters: economic assessment of ecosystem services as natural capital, ecosystem services assessment in term of accounting and management, biodiversity conservation in term of species richness, biodiversity conservation in term of human well-being, climate change and ecosystem services, and ecosystem services in urban area. Finally, citation network analysis was performed. 5700 publications consist of publications from the dataset and cited references from the publications were analyzed and 50 most influential articles from 1977 to 2018 with highest citation score was plotted in chronological order, providing insight on how the topic has been developing over time and important publications to be read. Bibliometric analysis proved to be very useful, especially as the preliminary step before conducting literature review. This technique can be very beneficial for early career scientists who wanted to recognize a field of science or wanted to know the research gaps that could be worked on.

Textile-based fiber optic sensors for health monitoring: A systematic and citation network analysis review

2021 ◽  
pp. 004051752110362
Author(s):  
Ka-Po Lee ◽  
Joanne Yip ◽  
Kit-Lun Yick ◽  
Chao Lu ◽  
Chris K Lo
Keyword(s):  
Network Analysis ◽  
Fiber Optic ◽  
Vital Signs ◽  
Citation Network ◽  
Fiber Optic Sensors ◽  
Superior Performance ◽  
Vital Signs Monitoring ◽  
Wear Comfort ◽  
Citation Network Analysis ◽  
Research Domains

Receptivity towards textile-based fiber optic sensors that are used to monitor physical health is increasing as they have good flexibility, are light in weight, provide wear comfort, have electromagnetic immunity, and are electrically safe. Their superior performance has facilitated their use for obtaining close to body measurements. However, there are many related studies in the literature, so it is challenging to identify the knowledge structure and research trends. Therefore, this article aims to provide an objective and systematic literature review on textile-based fiber optic sensors that are used for monitoring health issues and to analyze their trends through a citation network analysis. A full-text search of journal articles was conducted in the Web of Science Core Collection, and a total of 625 studies was found, with 47 that were used as the sample. Also, CitNetExplorer was used for analyzing the research domains and trends. Three research domains were identified, among them, “Flexible sensors for vital signs monitoring” is the largest research cluster, and most of the articles in this cluster focus on respiratory monitoring. Therefore, this area of study should probably be on the academic radar. The collection of data on textile-based fiber optic sensors is invaluable for evaluating degree of rehabilitation, detecting diseases, preventing accidents, as well as gauging the performance and training successfulness of athletes.

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