Jin Ge

1.4k citations
73 papers · 747 indexed · h-index 15

Jin Ge

59 papers receiving 730 citations

Peers

Jin Ge
Comparison fields: 5 of 128
  • Health Informatics 90
  • Hepatology 250
  • Transplantation 37
  • Geriatrics and Gerontology 35
  • Health Information Management 32
Replace Yasbanoo Moayedi with:
Yasbanoo Moayedi Canada
Jeremy A. Balch United States
Eva Schaden Austria
Prateeti Khazanie United States
Michael P. McRae United States
Guofeng Chen China
Gareth Jones United Kingdom
Babak Sabet Iran
Li Liang United States
Felix Schoenrath Germany
Jin Ge relative to Yasbanoo Moayedi Canada Yasbanoo Moayedi's profile →
Citations per field
00.5×5.2×
Yasbanoo Moayedi · 1×
Citations per year

Countries citing papers authored by Jin Ge

Since Specialization
Citations

This map shows the geographic impact of Jin Ge'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 Jin Ge with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Jin Ge more than expected).

Fields of papers citing papers by Jin Ge

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Jin Ge. 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 Jin Ge. The network helps show where Jin Ge may publish in the future.

Co-authorship network

The 25 scholars most cited alongside Jin Ge, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Jin Ge Line = papers co-authored together Jin Ge links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown
#Work
1 202510
2 20251
3 202469
4 20240
5 20242
6 20241
7 20241
8 202339
9 20231
10 20237
11 20233
12 202326
13 20236
14 20234
15 20231
16 202230
17 202129
18 20210
19
Distributed Access Control Scheme Based on Controlled Object in the Internet of Things
20121
20
The study on construction method of pipe-roofing tunel in saturated soft soil
20041

About Jin Ge

Jin Ge is a scholar working on Health Informatics, Hepatology, Transplantation, General Engineering and Health Information Management, having authored 73 papers that have together received 747 indexed citations. Recurring topics across this work include Liver Disease and Transplantation (21 papers), Organ Transplantation Techniques and Outcomes (18 papers), Liver Disease Diagnosis and Treatment (15 papers), Machine Learning in Healthcare (8 papers), COVID-19 Clinical Research Studies (5 papers), Renal Transplantation Outcomes and Treatments (4 papers), Artificial Intelligence in Healthcare and Education (4 papers) and Organ Donation and Transplantation (3 papers). The work is most often cited by research in Health Informatics (90 citations), Hepatology (250 citations), Transplantation (37 citations), Geriatrics and Gerontology (35 citations) and Health Information Management (32 citations). Jin Ge has collaborated with scholars based in United States, China and United Kingdom. Frequent co-authors include Jennifer C. Lai, Mark J. Pletcher, Jeremy Harper, Christopher G. Chute, Melissa Haendel, John C. Bucuvalas, Richard Gilroy, Michael Li, Joseph F. Owens and Allison J. Kwong. Their work appears in journals such as Hepatology, Hepatology Communications, American Journal of Transplantation, The American Journal of Gastroenterology and Gastroenterology.

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.

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