Matthias Hüser
- Artificial Intelligence top 10%
- Epidemiology
- Cardiology and Cardiovascular Medicine
- Surgery
- Health Informatics top 5%
- Co-authors
- Gunnar RätschMartin FaltysXinrui LyuMarc ZimmermannStephanie L. HylandBastian RieckKarsten BorgwardtThomas Gumbsch
- Topics
- Machine Learning in Healthcare (5 papers)Sepsis Diagnosis and Treatment (3 papers)Time Series Analysis and Forecasting (3 papers)
- Partner nations
- SwitzerlandNew ZealandUnited States
In The Last Decade
Matthias Hüser
9 papers receiving 252 citations
Hit Papers
Peers
Comparison fields: 5 of 85
- Artificial Intelligence 121
- Epidemiology 92
- Cardiology and Cardiovascular Medicine 48
- Surgery 45
- Health Informatics 38
Countries citing papers authored by Matthias Hüser
This map shows the geographic impact of Matthias Hüser'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 Matthias Hüser with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Matthias Hüser more than expected).
Fields of papers citing papers by Matthias Hüser
This network shows the impact of papers produced by Matthias Hüser. 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 Matthias Hüser. The network helps show where Matthias Hüser may publish in the future.
Co-authorship network of co-authors of Matthias Hüser
This figure shows the co-authorship network connecting the top 25 collaborators of Matthias Hüser. A scholar is included among the top collaborators of Matthias Hüser based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Matthias Hüser. Matthias Hüser is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 1 | |
| 2 | 3 | |
| 3 | 5 | |
| 4 | 6 | |
| 5 | Early prediction of circulatory failure in the intensive care unit using machine learningbreakdown → | 233 |
| 6 | Variational pSOM: Deep Probabilistic Clustering with Self-Organizing Maps | 1 |
| 7 | Predicting circulatory system deterioration in intensive care unit patients. | 2 |
| 8 | Deep Self-Organization: Interpretable Discrete Representation Learning on Time Series | 6 |
| 9 | Forecasting intracranial hypertension using waveform and time series features | 1 |
About Matthias Hüser
Matthias Hüser is a scholar working on Signal Processing, Artificial Intelligence and Nephrology, having authored 9 papers that have together received 258 indexed citations. Recurring topics across this work include Machine Learning in Healthcare (5 papers), Sepsis Diagnosis and Treatment (3 papers) and Time Series Analysis and Forecasting (3 papers). The work is most often cited by research in Health Informatics (38 citations), Family Practice (14 citations) and Health Information Management (25 citations). Matthias Hüser has collaborated with scholars based in Switzerland, New Zealand and United States. Frequent co-authors include Gunnar Rätsch, Martin Faltys, Xinrui Lyu, Marc Zimmermann, Stephanie L. Hyland, Bastian Rieck, Karsten Borgwardt, Thomas Gumbsch, Christian Bock and Max Horn. Their work appears in journals such as Nature Medicine, Bioinformatics and Pharmacoepidemiology and Drug Safety.
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.