scholarly journals Exploring the Development History and Research Tendency of Medical Informatics: Based on Topic Evolution Analysis (Preprint)

10.2196/31918 ◽  
2021 ◽  
Author(s):  
Wenting Han ◽  
Xi Han ◽  
Sijia Zhou ◽  
Qinghua Zhu
Keyword(s):  
Medical Informatics ◽  
Development History ◽  
Topic Evolution ◽  
Evolution Analysis

Topic Evolution Analysis Based on Cluster Topic Model

2016 ◽  
Vol 20 (1) ◽  
pp. 66-75
Author(s):  
Yaoyi Xi ◽  
◽  
Gang Chen ◽  
Bicheng Li ◽  
Yongwang Tang
Keyword(s):  
Semantic Information ◽  
Topic Model ◽  
Document Clustering ◽  
Number Of Clusters ◽  
Topic Evolution ◽  
Model Based ◽  
Evolution Analysis ◽  
Cluster Label

Topic evolution analysis helps to understand how the topics evolve or develop along the timeline. Aiming at the problem that existing researches did not mine the latent semantic information in depth and needed to pre-determine the number of clusters, this paper proposes cluster topic model based method to analyze topic evolution analysis. Firstly, a new topic model, namely cluster topic model, is built to complete document clustering while mining latent semantic information. Secondly, events are detected according to the cluster label of each document and evolution relationship between any two events is identified based on the aspect distributions of documents. Finally, by choosing the representative document of each event, topic evolution graph is constructed to display the development of the topic along the timeline. Experiments are presented to show the performance of our proposed technique. It is found that our proposed technique outperforms the comparable techniques in previous work.

Exploring the Development History and Research Tendency of Medical Informatics: Based on Topic Evolution Analysis (Preprint)

2021 ◽  
Author(s):  
Xi Han
Keyword(s):  
Medical Informatics ◽  
Rapid Development ◽  
Research Articles ◽  
Research Trend ◽  
Future Research ◽  
Research Topics ◽  
Topic Evolution ◽  
Evolution Pattern ◽  
Health Behavior Intervention ◽  
Research Hotspots

BACKGROUND Medical informatics has become a discipline that attracted researchers worldwide. It’s necessary to understand the development of its research hotspots and the future research trend. OBJECTIVE This research aimed to explore the evolution of research topics in medical informatics by analyzing relevant research articles published from 1964 to 2020. METHODS We collected research articles from 27 representative medical informatics journals indexed by the Web of Science Core Collection. The research topics of medical informatics were extracted based on LDA model and the topic evolution patterns were analyzed based on similarities between research topics. RESULTS A total of 56466 publications were identified. We found that medical informatics was in a period of rapid development. Health data analysis and health behavior intervention were the research hotspots all the time. While in recent years, the application of emerging computer technologies and mobile health tools attracted more research interests. CONCLUSIONS Our study provided a comprehensive understanding of the research hotspots and the evolution pattern among them in medical informatics, which was helpful for researchers to grasp research trends and design their studies.

scholarly journals Microblog topic evolution computing based on LDA algorithm

Open Physics ◽  
2018 ◽  
Vol 16 (1) ◽  
pp. 509-516 ◽  
Author(s):  
Feng Jian ◽  
Wang Yajiao ◽  
Ding Yuanyuan
Keyword(s):  
Latent Dirichlet Allocation ◽  
Evolutionary Relationship ◽  
Analysis Model ◽  
Calculation Algorithm ◽  
Topic Extraction ◽  
Topic Evolution ◽  
Evolution Analysis ◽  
Similarity Calculation ◽  
Relationship Of ◽  
Over Time

Abstract Research on topic evolution of Microblog is an effective way to analyze network public opinions. This paper proposes a method for mining changing of Microblog topics with time, and realizes topic evolution through topic extraction and topic relevance calculation. Firstly, latent Dirichlet allocation (LDA) model is used to automatically extract topics from different time slices; secondly, a similarity calculation algorithm is designed to calculate relevance of topic content through normalization of similarities among characteristic words and co-occurrence relations, to get evolutionary relationship among sub-topics of different time slices; thirdly, using probability distribution of blog article-topic to calculate topic intensity in each time slice, and then gets evolutionary relationship of topic intensity over time. Experiments show that the proposed topic evolution analysis model can effectively detect the evolution of topic content and intensity of real blogs.

scholarly journals Topic Evolution Analysis of COVID-19 News Articles

2020 ◽  
Vol 1601 ◽  
pp. 052009
Author(s):  
Yun Bai ◽  
Suling Jia ◽  
Lao Chen
Keyword(s):  
Topic Evolution ◽  
Evolution Analysis
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