Thomas Gumbsch
Impact in
- Health Informatics top 5%
- Artificial Intelligence in Healthcare and Education
Papers in
-
- Advanced Clustering Algorithms Research 2
- Machine Learning in Healthcare 1
- Anomaly Detection Techniques and Applications 1
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- Time Series Analysis and Forecasting 2
- Co-authors
- Karsten Borgwardt (8 shared papers)Christian Bock (5 shared papers)Bastian Rieck (5 shared papers)Michael Moor (4 shared papers)Max Horn (2 shared papers)Xinrui Lyu (1 shared paper)Cristóbal Esteban (1 shared paper)Dean A. Bodenham (1 shared paper)
- Journals
- Bioinformatics (3 papers)Nature Medicine (1 paper)Knowledge and Information Systems (1 paper)arXiv (Cornell University) (1 paper)Repository for Publications and Research Data (ETH Zurich) (1 paper)
- Partner nations
- SwitzerlandNew ZealandGermany
In The Last Decade
Thomas Gumbsch
8 papers receiving 286 citations
Thomas Gumbsch's Hit Papers
Peers
Comparison fields: 5 of 76
- Health Informatics 29
- Family Practice 8
- Health Information Management 21
- Artificial Intelligence 95
- Epidemiology 70
Countries citing papers authored by Thomas Gumbsch
This map shows the geographic impact of Thomas Gumbsch'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 Thomas Gumbsch with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Thomas Gumbsch more than expected).
Fields of papers citing papers by Thomas Gumbsch
This network shows the impact of papers produced by Thomas Gumbsch. 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 Thomas Gumbsch. The network helps show where Thomas Gumbsch may publish in the future.
Co-authors
The 16 scholars most cited alongside Thomas Gumbsch, 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 | Early prediction of circulatory failure in the intensive care unit using machine learning Hit paper breakdown → | 2020 | 250 |
| 2 | 2018 | 17 | |
| 3 | Neural Persistence: A Complexity Measure for Deep Neural Networks Using Algebraic Topology | 2019 | 10 |
| 4 | 2024 | 4 | |
| 5 | 2019 | 3 | |
| 6 | 2020 | 2 | |
| 7 | 2019 | 2 | |
| 8 | 2017 | 1 |
About Thomas Gumbsch
Thomas Gumbsch is a scholar working on Artificial Intelligence, Signal Processing, Surgery, Molecular Biology and Nephrology, having authored 8 papers that have together received 289 indexed citations. Recurring topics across this work include Advanced Clustering Algorithms Research (2 papers), Time Series Analysis and Forecasting (2 papers), Clusterin in disease pathology (1 paper), Hemodynamic Monitoring and Therapy (1 paper), Machine Learning in Healthcare (1 paper), Face and Expression Recognition (1 paper), Anomaly Detection Techniques and Applications (1 paper) and Metabolomics and Mass Spectrometry Studies (1 paper). The work is most often cited by research in Health Informatics (29 citations), Family Practice (8 citations), Health Information Management (21 citations), Artificial Intelligence (95 citations) and Epidemiology (70 citations). Thomas Gumbsch has collaborated with scholars based in Switzerland, New Zealand and Germany. Frequent co-authors include Karsten Borgwardt, Christian Bock, Bastian Rieck, Michael Moor, Max Horn, Xinrui Lyu, Cristóbal Esteban, Dean A. Bodenham, Martin Faltys and Stephanie L. Hyland. Their work appears in journals such as Bioinformatics, Nature Medicine, Knowledge and Information Systems, arXiv (Cornell University) and Repository for Publications and Research Data (ETH Zurich).
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.