David Kartchner
Impact in
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- Air Quality and Health Impacts
- Climate Change and Health Impacts
- Modeling and Simulation top 10%
- COVID-19 epidemiological studies
Papers in
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- Biomedical Text Mining and Ontologies 4
- Bioinformatics and Genomic Networks 2
- Machine Learning in Bioinformatics 1
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- Topic Modeling 5
- Natural Language Processing Techniques 2
- Machine Learning in Healthcare 2
- Co-authors
- Jacob S. Lefler (1 shared paper)Denitza Blagev (1 shared paper)Benjamin D. Horne (1 shared paper)Michelle Hofmann (1 shared paper)John B. Cannon (1 shared paper)Elizabeth A. Joy (1 shared paper)C. Arden Pope (1 shared paper)Per H. Gesteland (1 shared paper)
- Journals
- Cancers (1 paper)Big Data and Cognitive Computing (1 paper)Pharmaceutics (1 paper)Biology (1 paper)American Journal of Respiratory and Critical Care Medicine (1 paper)
- Partner nations
- United StatesNetherlandsCanada
In The Last Decade
David Kartchner
12 papers receiving 361 citations
David Kartchner's Hit Papers
Peers
Comparison fields: 5 of 87
- Health, Toxicology and Mutagenesis 219
- Modeling and Simulation 36
- Health Informatics 7
- Speech and Hearing 35
- Pollution 45
Countries citing papers authored by David Kartchner
This map shows the geographic impact of David Kartchner'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 Kartchner with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites David Kartchner more than expected).
Fields of papers citing papers by David Kartchner
This network shows the impact of papers produced by David Kartchner. 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 Kartchner. The network helps show where David Kartchner may publish in the future.
Co-authors
The 22 scholars most cited alongside David Kartchner, 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 | Short-Term Elevation of Fine Particulate Matter Air Pollution and Acute Lower Respiratory Infection Hit paper breakdown → | 2018 | 275 |
| 2 | 2021 | 23 | |
| 3 | 2023 | 16 | |
| 4 | 2018 | 15 | |
| 5 | 2022 | 11 | |
| 6 | 2017 | 8 | |
| 7 | 2023 | 6 | |
| 8 | 2023 | 4 | |
| 9 | 2022 | 4 | |
| 10 | 2023 | 3 | |
| 11 | 2023 | 2 | |
| 12 | 2017 | 2 | |
| 13 | 2025 | 0 |
About David Kartchner
David Kartchner is a scholar working on Molecular Biology, Artificial Intelligence, Infectious Diseases, Health Information Management and Computational Theory and Mathematics, having authored 13 papers that have together received 369 indexed citations. Recurring topics across this work include Topic Modeling (5 papers), Biomedical Text Mining and Ontologies (4 papers), Natural Language Processing Techniques (2 papers), Artificial Intelligence in Healthcare (2 papers), Computational Drug Discovery Methods (2 papers), Bioinformatics and Genomic Networks (2 papers), Machine Learning in Healthcare (2 papers) and Machine Learning in Bioinformatics (1 paper). The work is most often cited by research in Health, Toxicology and Mutagenesis (219 citations), Modeling and Simulation (36 citations), Health Informatics (7 citations), Speech and Hearing (35 citations) and Pollution (45 citations). David Kartchner has collaborated with scholars based in United States, Netherlands and Canada. Frequent co-authors include Jacob S. Lefler, Denitza Blagev, Benjamin D. Horne, Michelle Hofmann, John B. Cannon, Elizabeth A. Joy, C. Arden Pope, Per H. Gesteland, E. Kent Korgenski and Cassie S. Mitchell. Their work appears in journals such as Cancers, Big Data and Cognitive Computing, Pharmaceutics, Biology and American Journal of Respiratory and Critical Care Medicine.
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.