Thomas Gumbsch

510 total citations · 1 hit paper
8 papers, 270 citations indexed

About

Thomas Gumbsch is a scholar working on Artificial Intelligence, Molecular Biology and Signal Processing. According to data from OpenAlex, Thomas Gumbsch has authored 8 papers receiving a total of 270 indexed citations (citations by other indexed papers that have themselves been cited), including 6 papers in Artificial Intelligence, 2 papers in Molecular Biology and 2 papers in Signal Processing. Recurrent topics in Thomas Gumbsch's work include Machine Learning in Healthcare (3 papers), Metabolomics and Mass Spectrometry Studies (2 papers) and Sepsis Diagnosis and Treatment (2 papers). Thomas Gumbsch is often cited by papers focused on Machine Learning in Healthcare (3 papers), Metabolomics and Mass Spectrometry Studies (2 papers) and Sepsis Diagnosis and Treatment (2 papers). Thomas Gumbsch collaborates with scholars based in Switzerland, New Zealand and Australia. Thomas Gumbsch's co-authors include Karsten Borgwardt, Bastian Rieck, Christian Bock, Michael Moor, Max Horn, Marc Zimmermann, Stephanie L. Hyland, Xinrui Lyu, Matthias Hüser and Gunnar Rätsch and has published in prestigious journals such as Nature Medicine, Bioinformatics and Knowledge and Information Systems.

In The Last Decade

Thomas Gumbsch

8 papers receiving 266 citations

Hit Papers

align trajectories

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

Early prediction of circulatory failure in the intensive care unit using machine learning

2020 233 citations Stephanie L. Hyland, Martin Faltys et al. Nature Medicine profile →

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name	h						Papers	Cites
Thomas Gumbsch Switzerland	4	127	89	48	46	36	8	270
Matthias Hüser Switzerland	4	121 1.0×	92 1.0×	48 1.0×	45 1.0×	38 1.1×	9	258
Martin Faltys Switzerland	5	117 0.9×	93 1.0×	47 1.0×	45 1.0×	38 1.1×	8	258
Stephanie L. Hyland United States	5	162 1.3×	87 1.0×	61 1.3×	56 1.2×	71 2.0×	10	338
Piotr Jaroslaw Chmura Denmark	7	146 1.1×	109 1.2×	44 0.9×	55 1.2×	38 1.1×	14	335
Farah E. Shamout United Kingdom	8	107 0.8×	72 0.8×	27 0.6×	31 0.7×	21 0.6×	18	300
Max Horn Switzerland	9	211 1.7×	207 2.3×	57 1.2×	60 1.3×	63 1.8×	17	507
Francesca Raimondi United Kingdom	7	140 1.1×	59 0.7×	99 2.1×	32 0.7×	43 1.2×	8	371
Simon Meyer Lauritsen Denmark	5	251 2.0×	132 1.5×	39 0.8×	37 0.8×	82 2.3×	7	418
Rohit Joshi United States	7	206 1.6×	89 1.0×	20 0.4×	24 0.5×	18 0.5×	7	310
Meng-Ju Hsieh Taiwan	13	63 0.5×	34 0.4×	24 0.5×	29 0.6×	23 0.6×	21	340

Countries citing papers authored by Thomas Gumbsch

Since Specialization

Citations

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

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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-authorship network of co-authors of Thomas Gumbsch

This figure shows the co-authorship network connecting the top 25 collaborators of Thomas Gumbsch. A scholar is included among the top collaborators of Thomas Gumbsch 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 Thomas Gumbsch. Thomas Gumbsch is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

Sort: Min cites: Since: Top N: Style:

8 of 8 papers shown

1.

Hüser, Matthias, Thomas Gumbsch, Martin Faltys, et al.. (2024). An empirical study on KDIGO-defined acute kidney injury prediction in the intensive care unit. Bioinformatics. 40(Supplement_1). i247–i256. 3 indexed citations

2.

Hyland, Stephanie L., Martin Faltys, Matthias Hüser, et al.. (2020). Early prediction of circulatory failure in the intensive care unit using machine learning. Nature Medicine. 26(3). 364–373. 233 indexed citations breakdown →

3.

Gumbsch, Thomas, Christian Bock, Michael Moor, Bastian Rieck, & Karsten Borgwardt. (2020). Enhancing statistical power in temporal biomarker discovery through representative shapelet mining. Bioinformatics. 36(Supplement_2). i840–i848. 2 indexed citations

4.

Rieck, Bastian, Matteo Togninalli, Christian Bock, et al.. (2019). Neural Persistence: A Complexity Measure for Deep Neural Networks Using Algebraic Topology. arXiv (Cornell University). 10 indexed citations

5.

Gumbsch, Thomas, et al.. (2019). Kernel conditional clustering and kernel conditional semi-supervised learning. Knowledge and Information Systems. 62(3). 899–925. 2 indexed citations

6.

Bock, Christian, et al.. (2019). A Wasserstein Subsequence Kernel for Time Series. Repository for Publications and Research Data (ETH Zurich). 964–969. 3 indexed citations

7.

Bock, Christian, Thomas Gumbsch, Michael Moor, et al.. (2018). Association mapping in biomedical time series via statistically significant shapelet mining. Bioinformatics. 34(13). i438–i446. 16 indexed citations

8.

Gumbsch, Thomas, et al.. (2017). Kernel Conditional Clustering. 157–166. 1 indexed citations

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.

Explore authors with similar magnitude of impact