Automated Author ProfileChen, Haibo
Chen, Haibo
Current S-Index
Sum of Dataset Indices for all datasets
Average Dataset Index per Dataset
Average Dataset Index per dataset
Total Datasets
Total datasets for this author
Average FAIR Score
Average FAIR Score per dataset
Total Citations
Total citations to the author's datasets
Total Mentions
Total mentions of the author's datasets
S-Index Interpretation
The S-Index (Sharing Index) is a comprehensive metric that represents the cumulative impact of all your datasets. It is calculated as the sum of Dataset Index scores across all your claimed datasets.
What it means:
- A higher S-index indicates greater overall impact of your datasets relative to typical datasets in their fields of research
- The S-Index grows as you add more datasets or as existing datasets gain more citations and mentions
- It provides a single number to track your research data impact over time
Current S-Index: 1.4 (sum of 1 dataset Dataset Index scores)
More information here.
S-Index Over Time
Cumulative Citations Over Time
Cumulative Mentions Over Time
Datasets
A shortage of beds and cross-infection in hospitals due to patient crowding and overloading during the COVID-19 pandemic necessitate the use of telemedicine over face-to-face treatment. This study used statistical analysis to evaluate the impact of treatment choice among hospitals, patients, and the government to encourage them to employ telemedicine to avoid overload risk in the IoT environment during the pandemic by analyzing data from Tongji Hospital of Wuhan, China from January to September 2020. We then analyzed the patients’ choice of medical treatment, evolution process, and influencing factors of the corresponding hospital overload risk by transforming the game model into a cusp catastrophe model. Finally, a tripartite cooperative game model was used to establish a cooperative mechanism in which patients, hospitals, and the government jointly accepted telemedicine to avoid overload risks.
Authors
- Shi, Siwei ;
- Chen, Haibo