Alvin Rajkomar
- Health Informatics top 0.02%
- Health Information Management top 0.2%
- Electronic Health Records Systems 3
- Family Practice top 2%
- Clinical Reasoning and Diagnostic Skills 3
- Artificial Intelligence top 1%
- Machine Learning in Healthcare 5
- Topic Modeling 3
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- Healthcare cost, quality, practices 4
- Primary Care and Health Outcomes 3
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- Healthcare Systems and Technology 3
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- Biomedical Text Mining and Ontologies 2
- Co-authors
- Jay B. DeanIsaac S. KohaneMichaela HardtMarshall H. ChinGreg S. CorradoMichael HowellJohn MonganAndrew Taylor
- Journals
- Journal of Hospital Medicine (4 papers)Annals of Internal Medicine (2 papers)Journal of Digital Imaging (1 paper)
- Partner nations
- United StatesSwitzerlandRussia
In The Last Decade
Alvin Rajkomar
23 papers receiving 3.1k citations
Hit Papers
Peers
Comparison fields: 5 of 181
- Health Informatics 959
- Health Information Management 456
- Family Practice 116
- Radiology, Nuclear Medicine and Imaging 727
- Artificial Intelligence 880
Countries citing papers authored by Alvin Rajkomar
This map shows the geographic impact of Alvin Rajkomar'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 Alvin Rajkomar with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Alvin Rajkomar more than expected).
Fields of papers citing papers by Alvin Rajkomar
This network shows the impact of papers produced by Alvin Rajkomar. 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 Alvin Rajkomar. The network helps show where Alvin Rajkomar may publish in the future.
Co-authorship network
The 25 scholars most cited alongside Alvin Rajkomar, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | 2023 | 1 | |
| 2 | 2022 | 10 | |
| 3 | 2020 | 16 | |
| 4 | Improved Patient Classification with Language Model Pretraining Over Clinical Notes. | 2019 | 2 |
| 5 | Machine Learning in Medicinebreakdown → | 2019 | 2069 |
| 6 | 2019 | 36 | |
| 7 | Ensuring Fairness in Machine Learning to Advance Health Equitybreakdown → | 2018 | 551 |
| 8 | 2018 | 10 | |
| 9 | 2018 | 16 | |
| 10 | 2018 | 21 | |
| 11 | 2017 | 13 | |
| 12 | 2017 | 30 | |
| 13 | 2017 | 11 | |
| 14 | 2016 | 10 | |
| 15 | 2016 | 21 | |
| 16 | 2016 | 5 | |
| 17 | 2016 | 85 | |
| 18 | 2016 | 100 | |
| 19 | 2016 | 15 | |
| 20 | 2015 | 29 |
About Alvin Rajkomar
Alvin Rajkomar is a scholar working on Health Informatics, Family Practice, Health Information Management, Geriatrics and Gerontology and General Health Professions, having authored 23 papers that have together received 3.1k indexed citations. Recurring topics across this work include Machine Learning in Healthcare (5 papers), Healthcare cost, quality, practices (4 papers), Topic Modeling (3 papers), Clinical Reasoning and Diagnostic Skills (3 papers), Healthcare Systems and Technology (3 papers), Primary Care and Health Outcomes (3 papers), Electronic Health Records Systems (3 papers) and Biomedical Text Mining and Ontologies (2 papers). The work is most often cited by research in Health Informatics (959 citations), Health Information Management (456 citations), Family Practice (116 citations), Radiology, Nuclear Medicine and Imaging (727 citations) and Artificial Intelligence (880 citations). Alvin Rajkomar has collaborated with scholars based in United States, Switzerland and Russia. Frequent co-authors include Jay B. Dean, Isaac S. Kohane, Michaela Hardt, Marshall H. Chin, Greg S. Corrado, Michael Howell, John Mongan, Andrew Taylor, Victoria Valencia and Gurpreet Dhaliwal. Their work appears in journals such as Journal of Hospital Medicine, Annals of Internal Medicine, Journal of Digital Imaging, Nature Communications and Clinical Pharmacology & Therapeutics.
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.