Ki-Seong Lee
Biographic Data
| ID | 7862764 |
|---|---|
| NAME | Ki-Seong Lee |
| GIVEN NAMES | Ki-Seong |
| FAMILY NAME | Lee |
| SIGNATURE | LEE K |
| AFFILIATIONS | Chung-Ang University |
| ORCID | 0000-0002-0906-9552 |
| VERIFIED | Yes |
| TOTAL WORKS | 2 |
| TOTAL CITATIONS | 0 |
| WORKS AS AUTHOR | 2 |
| WORKS AS EDITOR | 0 |
| FIRST PUBLICATION YEAR | 2020 |
| LATEST PUBLICATION YEAR | 2021 |
| H-INDEX | 0 |
A Network Analysis of Research Topics and Trends in End-of-Life Care and Nursing
This study identified the trends in end-of-life care and nursing through text network analysis. About 18,935 articles published until September 2019 were selected through searches on PubMed, Embase, Cochrane, Web of Science, and Cumulative Index to Nursing and Allied Health Literature. For topic modeling, Latent Dirichlet Allocation (K = 8) was applied. Most of the top ranked topic words for the degree and betweenness centralities were consistent…
Identification of the Knowledge Structure of Cancer Survivors’ Return to Work and Quality of Life
This study aimed to understand the trends in research on the quality of life of returning to work (RTW) cancer survivors using text network analysis. Titles and abstracts of each article were examined to extract terms, including "cancer survivors", "return to work", and "quality of life", which were found in 219 articles published between 1990 and June 2020. Python and Gephi software were used to analyze the data and visualize the networks. Keywo…
No prominent works on this page.
Identification of the Knowledge Structure of Cancer Survivors’ Return to Work and Quality of Life
This study aimed to understand the trends in research on the quality of life of returning to work (RTW) cancer survivors using text network analysis. Titles and abstracts of each article were examined to extract terms, including "cancer survivors", "return to work", and "quality of life", which were found in 219 articles published between 1990 and June 2020. Python and Gephi software were used to analyze the data and visualize the networks. Keywo…
A Network Analysis of Research Topics and Trends in End-of-Life Care and Nursing
This study identified the trends in end-of-life care and nursing through text network analysis. About 18,935 articles published until September 2019 were selected through searches on PubMed, Embase, Cochrane, Web of Science, and Cumulative Index to Nursing and Allied Health Literature. For topic modeling, Latent Dirichlet Allocation (K = 8) was applied. Most of the top ranked topic words for the degree and betweenness centralities were consistent…
Artificial Intelligence (2 works) · Betweenness centrality (2 works) · Computer Science (2 works) · Information retrieval (2 works) · Medicine (2 works) · Psychology (2 works) · Applied Psychology (1 work) · Applied Psychology (1 work) · Bioinformatics and Genomic Networks (1 work) · Centrality (1 work)