Xinge Ji
Impact in
- Health Informatics top 5%
- Infectious Diseases top 10%
- COVID-19 Clinical Research Studies
- SARS-CoV-2 and COVID-19 Research
Papers in
-
- Diabetes Treatment and Management 11
- Diabetes Management and Research 8
- Oncology 8
- COVID-19 and healthcare impacts 4
- Co-authors
- Michael W. Kattan (28 shared papers)Alex Milinovich (20 shared papers)Lara Jehi (7 shared papers)James B. Young (5 shared papers)Steve Gordon (3 shared papers)Janine Bauman (13 shared papers)Anita D. Misra‐Hebert (12 shared papers)Kevin M. Pantalone (12 shared papers)
- Journals
- Journal of Diabetes and its Complications (3 papers)Diabetes Care (3 papers)Endocrine Practice (2 papers)Cancer Medicine (2 papers)CHEST Journal (2 papers)
- Partner nations
- United StatesJapanItaly
In The Last Decade
Xinge Ji
32 papers receiving 692 citations
Peers
Comparison fields: 5 of 96
- Health Informatics 23
- Infectious Diseases 178
- Endocrinology, Diabetes and Metabolism 150
- Modeling and Simulation 36
- Health Information Management 36
Countries citing papers authored by Xinge Ji
This map shows the geographic impact of Xinge Ji's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Xinge Ji with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Xinge Ji more than expected).
Fields of papers citing papers by Xinge Ji
This network shows the impact of papers produced by Xinge Ji. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Xinge Ji. The network helps show where Xinge Ji may publish in the future.
Co-authors
The 25 scholars most cited alongside Xinge Ji, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
Showing the 20 most-cited of 35 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | 2020 | 123 | |
| 2 | 2020 | 93 | |
| 3 | 2019 | 71 | |
| 4 | 2018 | 38 | |
| 5 | 2018 | 37 | |
| 6 | 2020 | 34 | |
| 7 | 2020 | 33 | |
| 8 | 2020 | 29 | |
| 9 | 2021 | 27 | |
| 10 | 2017 | 26 | |
| 11 | 2019 | 25 | |
| 12 | 2020 | 21 | |
| 13 | 2019 | 21 | |
| 14 | 2019 | 18 | |
| 15 | 2020 | 17 | |
| 16 | 2019 | 14 | |
| 17 | 2019 | 12 | |
| 18 | 2021 | 11 | |
| 19 | 2022 | 11 | |
| 20 | 2020 | 10 |
About Xinge Ji
Xinge Ji is a scholar working on Endocrinology, Diabetes and Metabolism, Oncology, Epidemiology, Infectious Diseases and Surgery, having authored 35 papers that have together received 713 indexed citations. Recurring topics across this work include Diabetes Treatment and Management (11 papers), Diabetes Management and Research (8 papers), COVID-19 Clinical Research Studies (6 papers), COVID-19 and healthcare impacts (4 papers), Helicobacter pylori-related gastroenterology studies (3 papers), Heart Failure Treatment and Management (3 papers), COVID-19 diagnosis using AI (2 papers) and Diabetes and associated disorders (2 papers). The work is most often cited by research in Health Informatics (23 citations), Infectious Diseases (178 citations), Endocrinology, Diabetes and Metabolism (150 citations), Modeling and Simulation (36 citations) and Health Information Management (36 citations). Xinge Ji has collaborated with scholars based in United States, Japan and Italy. Frequent co-authors include Michael W. Kattan, Alex Milinovich, Lara Jehi, James B. Young, Steve Gordon, Janine Bauman, Anita D. Misra‐Hebert, Kevin M. Pantalone, Brian P. Rubin and Robert S. Zimmerman. Their work appears in journals such as Journal of Diabetes and its Complications, Diabetes Care, Endocrine Practice, Cancer Medicine and CHEST Journal.
Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.