Mohammad Mamouei
Impact in
- Health Informatics top 10%
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- Artificial Intelligence in Healthcare
Papers in
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- Spectroscopy Techniques in Biomedical and Chemical Research 8
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- Machine Learning in Healthcare 6
- Co-authors
- Kazem Rahimi (14 shared papers)P. A. Kyriacou (14 shared papers)Karthik Budidha (12 shared papers)Gholamreza Salimi‐Khorshidi (10 shared papers)Meha Qassem (12 shared papers)Abdelǎali Hassaïne (8 shared papers)Shishir Rao (11 shared papers)Yikuan Li (10 shared papers)
- Journals
- Scientific Reports (5 papers)Sensors (3 papers)iScience (1 paper)The Lancet Digital Health (1 paper)IEEE Journal of Biomedical and Health Informatics (1 paper)
- Partner nations
- United KingdomAustraliaChina
In The Last Decade
Mohammad Mamouei
33 papers receiving 375 citations
Peers
Comparison fields: 5 of 117
- Health Informatics 14
- Health Information Management 41
- Biophysics 28
- Transportation 28
- Analytical Chemistry 37
Countries citing papers authored by Mohammad Mamouei
This map shows the geographic impact of Mohammad Mamouei'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 Mohammad Mamouei with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Mohammad Mamouei more than expected).
Fields of papers citing papers by Mohammad Mamouei
This network shows the impact of papers produced by Mohammad Mamouei. 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 Mohammad Mamouei. The network helps show where Mohammad Mamouei may publish in the future.
Co-authors
The 25 scholars most cited alongside Mohammad Mamouei, 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 34 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | 2022 | 65 | |
| 2 | 2023 | 35 | |
| 3 | 2021 | 29 | |
| 4 | 2021 | 23 | |
| 5 | 2018 | 23 | |
| 6 | 2022 | 21 | |
| 7 | 2020 | 20 | |
| 8 | 2022 | 20 | |
| 9 | 2020 | 18 | |
| 10 | 2023 | 12 | |
| 11 | 2021 | 12 | |
| 12 | 2022 | 11 | |
| 13 | 2022 | 10 | |
| 14 | 2023 | 9 | |
| 15 | 2020 | 9 | |
| 16 | 2021 | 9 | |
| 17 | 2022 | 9 | |
| 18 | 2022 | 8 | |
| 19 | 2020 | 6 | |
| 20 | 2023 | 5 |
About Mohammad Mamouei
Mohammad Mamouei is a scholar working on Biophysics, Artificial Intelligence, Surgery, Analytical Chemistry and Control and Systems Engineering, having authored 34 papers that have together received 382 indexed citations. Recurring topics across this work include Spectroscopy Techniques in Biomedical and Chemical Research (8 papers), Spectroscopy and Chemometric Analyses (6 papers), Machine Learning in Healthcare (6 papers), Hemodynamic Monitoring and Therapy (5 papers), Optical Imaging and Spectroscopy Techniques (5 papers), Vehicle emissions and performance (3 papers), Artificial Intelligence in Healthcare (3 papers) and Non-Invasive Vital Sign Monitoring (3 papers). The work is most often cited by research in Health Informatics (14 citations), Health Information Management (41 citations), Biophysics (28 citations), Transportation (28 citations) and Analytical Chemistry (37 citations). Mohammad Mamouei has collaborated with scholars based in United Kingdom, Australia and China. Frequent co-authors include Kazem Rahimi, P. A. Kyriacou, Karthik Budidha, Gholamreza Salimi‐Khorshidi, Meha Qassem, Abdelǎali Hassaïne, Shishir Rao, Yikuan Li, Dexter Canoy and Thomas Lukasiewicz. Their work appears in journals such as Scientific Reports, Sensors, iScience, The Lancet Digital Health and IEEE Journal of Biomedical and Health Informatics.
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.