Felix Gräßer
Impact in
- Family Practice top 10%
Papers in
-
- Recommender Systems and Techniques 4
-
- Machine Learning in Healthcare 4
- Co-authors
- Hagen Malberg (10 shared papers)Sebastian Zaunseder (9 shared papers)Surya Kallumadi (1 shared paper)Fernando Andreotti (1 shared paper)Susanne Abraham (4 shared papers)Jochen Schmitt (4 shared papers)Denise Küster (2 shared papers)Karen Voigt (1 shared paper)
- Journals
- BMC Medical Informatics and Decision Making (2 papers)Computers in Biology and Medicine (1 paper)IEEE Transactions on Biomedical Engineering (1 paper)Biomedical Signal Processing and Control (1 paper)User Modeling and User-Adapted Interaction (1 paper)
- Partner nations
- GermanyUnited KingdomAustralia
In The Last Decade
Felix Gräßer
12 papers receiving 360 citations
Peers
Comparison fields: 5 of 86
- Family Practice 19
- Health Informatics 9
- Artificial Intelligence 206
- Health Information Management 29
- Cardiology and Cardiovascular Medicine 62
Countries citing papers authored by Felix Gräßer
This map shows the geographic impact of Felix Gräßer'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 Felix Gräßer with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Felix Gräßer more than expected).
Fields of papers citing papers by Felix Gräßer
This network shows the impact of papers produced by Felix Gräßer. 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 Felix Gräßer. The network helps show where Felix Gräßer may publish in the future.
Co-authors
The 22 scholars most cited alongside Felix Gräßer, 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 | 118 | |
| 2 | 2019 | 102 | |
| 3 | 2017 | 55 | |
| 4 | 2017 | 30 | |
| 5 | 2018 | 18 | |
| 6 | Neighborhood-based Collaborative Filtering for Therapy Decision Support. | 2017 | 15 |
| 7 | 2021 | 11 | |
| 8 | 2021 | 7 | |
| 9 | 2023 | 6 | |
| 10 | 2019 | 3 | |
| 11 | 2020 | 3 | |
| 12 | 2019 | 1 | |
| 13 | 2023 | 0 |
About Felix Gräßer
Felix Gräßer is a scholar working on Information Systems, Artificial Intelligence, Cardiology and Cardiovascular Medicine, Cognitive Neuroscience and Molecular Biology, having authored 13 papers that have together received 369 indexed citations. Recurring topics across this work include Recommender Systems and Techniques (4 papers), Machine Learning in Healthcare (4 papers), EEG and Brain-Computer Interfaces (3 papers), ECG Monitoring and Analysis (3 papers), Biomedical Text Mining and Ontologies (2 papers), Non-Invasive Vital Sign Monitoring (2 papers), Phonocardiography and Auscultation Techniques (2 papers) and Digital Imaging for Blood Diseases (1 paper). The work is most often cited by research in Family Practice (19 citations), Health Informatics (9 citations), Artificial Intelligence (206 citations), Health Information Management (29 citations) and Cardiology and Cardiovascular Medicine (62 citations). Felix Gräßer has collaborated with scholars based in Germany, United Kingdom and Australia. Frequent co-authors include Hagen Malberg, Sebastian Zaunseder, Surya Kallumadi, Fernando Andreotti, Susanne Abraham, Jochen Schmitt, Denise Küster, Karen Voigt, Falko Tesch and Marietta Zille. Their work appears in journals such as BMC Medical Informatics and Decision Making, Computers in Biology and Medicine, IEEE Transactions on Biomedical Engineering, Biomedical Signal Processing and Control and User Modeling and User-Adapted Interaction.
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.