Parag Jain
Impact in
- Health Informatics top 5%
- Nephrology top 5%
- Acute Kidney Injury Research
Papers in
-
- Topic Modeling 6
- Natural Language Processing Techniques 6
- AI in cancer detection 4
- Epidemiology 10
- Sepsis Diagnosis and Treatment 6
- Co-authors
- Soumitro Banerjee (1 shared paper)M. Ajazuddin (7 shared papers)Natalie Z. Cvijanovich (11 shared papers)Neal J. Thomas (12 shared papers)Riad Lutfi (11 shared papers)Geoffrey L. Allen (7 shared papers)Scott L. Weiss (12 shared papers)Adam J. Schwarz (11 shared papers)
- Journals
- Pediatric Critical Care Medicine (6 papers)JCO Clinical Cancer Informatics (3 papers)Critical Care (3 papers)Journal of Clinical Oncology (3 papers)Current Drug Delivery (2 papers)
- Partner nations
- United StatesIndiaAustralia
In The Last Decade
Parag Jain
63 papers receiving 714 citations
Peers
Comparison fields: 5 of 126
- Health Informatics 24
- Nephrology 80
- Critical Care and Intensive Care Medicine 25
- Emergency Medicine 42
- Statistical and Nonlinear Physics 62
Countries citing papers authored by Parag Jain
This map shows the geographic impact of Parag Jain'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 Parag Jain with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Parag Jain more than expected).
Fields of papers citing papers by Parag Jain
This network shows the impact of papers produced by Parag Jain. 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 Parag Jain. The network helps show where Parag Jain may publish in the future.
Co-authors
The 25 scholars most cited alongside Parag Jain, 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 75 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | 2003 | 100 | |
| 2 | 2014 | 52 | |
| 3 | 2019 | 46 | |
| 4 | 2020 | 45 | |
| 5 | 2021 | 43 | |
| 6 | 2019 | 31 | |
| 7 | 2023 | 27 | |
| 8 | 2024 | 27 | |
| 9 | 2023 | 26 | |
| 10 | 2023 | 26 | |
| 11 | 2017 | 24 | |
| 12 | 2022 | 18 | |
| 13 | 2021 | 18 | |
| 14 | 2022 | 18 | |
| 15 | 2023 | 16 | |
| 16 | 2023 | 15 | |
| 17 | 2019 | 14 | |
| 18 | 2022 | 14 | |
| 19 | 2023 | 14 | |
| 20 | 2023 | 13 |
About Parag Jain
Parag Jain is a scholar working on Artificial Intelligence, Epidemiology, Surgery, Radiology, Nuclear Medicine and Imaging and Nephrology, having authored 75 papers that have together received 738 indexed citations. Recurring topics across this work include Topic Modeling (6 papers), Natural Language Processing Techniques (6 papers), Sepsis Diagnosis and Treatment (6 papers), Acute Kidney Injury Research (6 papers), Radiomics and Machine Learning in Medical Imaging (5 papers), AI in cancer detection (4 papers), Advanced Drug Delivery Systems (3 papers) and Simulation-Based Education in Healthcare (2 papers). The work is most often cited by research in Health Informatics (24 citations), Nephrology (80 citations), Critical Care and Intensive Care Medicine (25 citations), Emergency Medicine (42 citations) and Statistical and Nonlinear Physics (62 citations). Parag Jain has collaborated with scholars based in United States, India and Australia. Frequent co-authors include Soumitro Banerjee, M. Ajazuddin, Natalie Z. Cvijanovich, Neal J. Thomas, Riad Lutfi, Geoffrey L. Allen, Scott L. Weiss, Adam J. Schwarz, Michael T. Bigham and Jeffrey Nowak. Their work appears in journals such as Pediatric Critical Care Medicine, JCO Clinical Cancer Informatics, Critical Care, Journal of Clinical Oncology and Current Drug Delivery.
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.