Peter M. Full
Impact in
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- Artificial Intelligence in Healthcare and Education
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- Topic Modeling
- Machine Learning in Healthcare
- Anomaly Detection Techniques and Applications
- Natural Language Processing Techniques
Papers in
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- Biomedical Text Mining and Ontologies 2
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- Topic Modeling 2
- Natural Language Processing Techniques 1
- Co-authors
- Klaus Maier‐Hein (3 shared papers)Jens Kleesiek (2 shared papers)Gregor Koehler (1 shared paper)Annika Reinke (2 shared papers)Paul F. Jäger (2 shared papers)Michael Strube (1 shared paper)Tim Frederik Weber (1 shared paper)Lena Maier‐Hein (2 shared papers)
- Journals
- Medical Image Analysis (1 paper)The Journal of Clinical Endocrinology & Metabolism (1 paper)JMIR Medical Informatics (1 paper)Zenodo (CERN European Organization for Nuclear Research) (2 papers)
- Partner nations
- GermanyUnited StatesNetherlands
In The Last Decade
Peter M. Full
5 papers receiving 34 citations
Peers
Comparison fields: 5 of 30
- Health Informatics 5
- Artificial Intelligence 24
- Transplantation 2
- Radiology, Nuclear Medicine and Imaging 9
- Geriatrics and Gerontology 1
Countries citing papers authored by Peter M. Full
This map shows the geographic impact of Peter M. Full'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 Peter M. Full with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Peter M. Full more than expected).
Fields of papers citing papers by Peter M. Full
This network shows the impact of papers produced by Peter M. Full. 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 Peter M. Full. The network helps show where Peter M. Full may publish in the future.
Co-authors
The 25 scholars most cited alongside Peter M. Full, 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 | 2020 | 15 | |
| 2 | 2023 | 7 | |
| 3 | 2022 | 7 | |
| 4 | 2020 | 5 | |
| 5 | 2025 | 1 | |
| 6 | 2020 | 0 |
About Peter M. Full
Peter M. Full is a scholar working on Molecular Biology, Artificial Intelligence, Nephrology, General Health Professions and Computer Vision and Pattern Recognition, having authored 6 papers that have together received 35 indexed citations. Recurring topics across this work include Topic Modeling (2 papers), Biomedical Text Mining and Ontologies (2 papers), Colorectal Cancer Screening and Detection (1 paper), Natural Language Processing Techniques (1 paper), Radiomics and Machine Learning in Medical Imaging (1 paper), Healthcare cost, quality, practices (1 paper), Parathyroid Disorders and Treatments (1 paper) and Magnesium in Health and Disease (1 paper). The work is most often cited by research in Health Informatics (5 citations), Artificial Intelligence (24 citations), Transplantation (2 citations), Radiology, Nuclear Medicine and Imaging (9 citations) and Geriatrics and Gerontology (1 citation). Peter M. Full has collaborated with scholars based in Germany, United States and Netherlands. Frequent co-authors include Klaus Maier‐Hein, Jens Kleesiek, Gregor Koehler, Annika Reinke, Paul F. Jäger, Michael Strube, Tim Frederik Weber, Lena Maier‐Hein, Tobias L. Roß and Tim Adler. Their work appears in journals such as Medical Image Analysis, The Journal of Clinical Endocrinology & Metabolism, JMIR Medical Informatics and Zenodo (CERN European Organization for Nuclear Research).
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.