Analysis of Hot Spots and Trends of Chinese Medical Maintenance Combination based on Sent-LDA Model
DOI:
https://doi.org/10.54097/hbem.v9i.7765Keywords:
Combination of Medical and Nursing Care; Sent-LDA Model; Text Mining; Research Trend.Abstract
To analyze the hot spots and trends of periodical texts on the topic of medical and maintenance combination can provide the basis to grasp the hot spots of research in this field and promote its further development. With the method of machine learning, 1,459 fund literatures with the theme of "combination of medical and nursing care" in CNKI database from 2013 to 2021 were taken as analysis samples. python was used to crawl the text content and preprocess it. Subject words were selected based on Sent-LDA model, and data were substituted to form a visualization map. Analyze the research trend of Chinese medical combination. Study found that at present our country medical have combined with the research hotspots focus on the allocation of resources, wisdom, provide for the aged, personnel management and rural development, then can pay attention to the field of policy implementation, cross-sectoral coordination mechanism, the old man nursing assessment system and grading system of diagnosis and treatment research.
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References
Si Xiaohong. Statistical analysis and prospect of research hotspots on balanced development of compulsory education [J]. Journal of Education,2015,11(06):49-59.
Wang Xiaoxiao, Guo Qing. Analysis on the Hot spot and development Trend of medical care Combination based on CiteSpace in the past 10 years [J]. Chinese Journal of General Practice, 201,24(01):92-97.
Zhang Zeyu, Tian Chong, Fang Pengqian. Analysis on the Development Trend of medical care Combination System during the "14th Five-Year Plan" based on healthy aging [J]. Chinese Journal of Public Health Administration, 201,38(07):493-495.
Li Zi, Qian Siwen, Pu Haixia. Analysis on the Hot spot and Development Trend of Chinese Medical Maintenance Combination based on CiteSpace [J]. Journal of Geriatric Research,202,2(05):27-32.
Yang Jinghui. Research hotspot and evolution trend of Chinese Medical Care combined pension model: A visualization analysis based on CiteSpace software [J]. Scientific Research on Aging, 201,9(02):51-64.
Bi Dayan, Chu Qihuan, Cao Ran. Research on the Influencing Factors of Consumers' Intention to Negative Review based on Text Mining [J]. Information Theory & Practice,20,43(10):137-143. (in Chinese)
Lu Jingjun, Hu Shunyi, Yu Jianguang. Method and demonstration of online public opinion Monitoring in Universities based on LDA-BiLSTM model [J]. Information Theory and Practice,20,43(11):156-161.
Yan Duanwu, Yang Xiongfei, Li Tiejun. Product Review Sentiment Analysis based on Product Feature Tree and LSTM Model [J]. Information Theory & Practice,2019,42(12):134-138. (in Chinese)
Sun Yusheng, Zhu Bo, Zhu Lijun. Research progress of information recommendation based on Knowledge graph [J]. Information Theory & Practice,2019,42(12):163-169+149.
Li Bin, Wang Yinghui, Zhu Xiaoqian, Li Jianping. Identification and Evolution Analysis of important risk Points in Insurance Industry -- Based on text risk information disclosed in financial Report [J]. Systems Engineering Theory and Practice,2021,(05):1-15.
Travis Dyer, Mark Lang, Lorien Stice-Lawrence. The evolution of 10-K textual disclosure: Evidence from Latent Dirichlet Allocation [J]. Journal of Accounting and Economics,2017,64(2-3): 221-245.
Bao Y, Datta A. Simultaneously Discovering and Quantifying Risk Types from Textual Risk Disclosures [J]. Management Science,2014,60(6): 1371-1391.
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