David A. Mong
Impact in
- Health Informatics top 10%
- Artificial Intelligence in Healthcare and Education
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- Topic Modeling
- Machine Learning in Healthcare
- Text and Document Classification Technologies
- Sentiment Analysis and Opinion Mining
Papers in
-
- Lung Cancer Diagnosis and Treatment 2
- Pulmonary Hypertension Research and Treatments 2
- Coronary Artery Anomalies 2
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- Congenital Heart Disease Studies 6
- Co-authors
- Brian E. Chapman (1 shared paper)Matthew P. Lungren (1 shared paper)Sadid A. Hasan (1 shared paper)N Moradzadeh (1 shared paper)Curtis P. Langlotz (1 shared paper)Daniel L. Rubin (1 shared paper)Timothy J. Amrhein (1 shared paper)Oladimeji Farri (1 shared paper)
- Journals
- Artificial Intelligence in Medicine (1 paper)Pediatric Rheumatology (1 paper)Medical Image Analysis (1 paper)Pediatric Anesthesia (1 paper)Seminars in Thoracic and Cardiovascular Surgery (1 paper)
- Partner nations
- United States
In The Last Decade
David A. Mong
10 papers receiving 223 citations
Peers
Comparison fields: 5 of 84
- Health Informatics 22
- Artificial Intelligence 89
- Health Information Management 12
- Radiology, Nuclear Medicine and Imaging 58
- Internal Medicine 6
Countries citing papers authored by David A. Mong
This map shows the geographic impact of David A. Mong'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 David A. Mong with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites David A. Mong more than expected).
Fields of papers citing papers by David A. Mong
This network shows the impact of papers produced by David A. Mong. 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 David A. Mong. The network helps show where David A. Mong may publish in the future.
Co-authors
The 25 scholars most cited alongside David A. Mong, 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 | 2018 | 186 | |
| 2 | 2016 | 24 | |
| 3 | 2020 | 7 | |
| 4 | 2022 | 4 | |
| 5 | 2023 | 4 | |
| 6 | 2021 | 3 | |
| 7 | 2023 | 1 | |
| 8 | 2022 | 1 | |
| 9 | 2022 | 1 | |
| 10 | 2025 | 1 | |
| 11 | 2023 | 0 |
About David A. Mong
David A. Mong is a scholar working on Pulmonary and Respiratory Medicine, Epidemiology, Surgery, Rheumatology and Radiology, Nuclear Medicine and Imaging, having authored 11 papers that have together received 232 indexed citations. Recurring topics across this work include Congenital Heart Disease Studies (6 papers), Lung Cancer Diagnosis and Treatment (2 papers), Pulmonary Hypertension Research and Treatments (2 papers), Coronary Artery Anomalies (2 papers), Eosinophilic Disorders and Syndromes (2 papers), Congenital Diaphragmatic Hernia Studies (2 papers), Cardiac Imaging and Diagnostics (2 papers) and Connective tissue disorders research (1 paper). The work is most often cited by research in Health Informatics (22 citations), Artificial Intelligence (89 citations), Health Information Management (12 citations), Radiology, Nuclear Medicine and Imaging (58 citations) and Internal Medicine (6 citations). David A. Mong has collaborated with scholars based in United States. Frequent co-authors include Brian E. Chapman, Matthew P. Lungren, Sadid A. Hasan, N Moradzadeh, Curtis P. Langlotz, Daniel L. Rubin, Timothy J. Amrhein, Oladimeji Farri, Ling Yuan and Imon Banerjee. Their work appears in journals such as Artificial Intelligence in Medicine, Pediatric Rheumatology, Medical Image Analysis, Pediatric Anesthesia and Seminars in Thoracic and Cardiovascular Surgery.
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.