Max Horn

2.2k total citations · 1 hit paper
17 papers, 507 citations indexed

About

Max Horn is a scholar working on Artificial Intelligence, Epidemiology and Computer Vision and Pattern Recognition. According to data from OpenAlex, Max Horn has authored 17 papers receiving a total of 507 indexed citations (citations by other indexed papers that have themselves been cited), including 8 papers in Artificial Intelligence, 7 papers in Epidemiology and 4 papers in Computer Vision and Pattern Recognition. Recurrent topics in Max Horn's work include Machine Learning in Healthcare (6 papers), Sepsis Diagnosis and Treatment (5 papers) and Clinical Reasoning and Diagnostic Skills (3 papers). Max Horn is often cited by papers focused on Machine Learning in Healthcare (6 papers), Sepsis Diagnosis and Treatment (5 papers) and Clinical Reasoning and Diagnostic Skills (3 papers). Max Horn collaborates with scholars based in Switzerland, United States and Germany. Max Horn's co-authors include Bastian Rieck, Karsten Borgwardt, Michael Moor, Catherine R. Jutzeler, Thomas Gumbsch, Christian Bock, Marc Zimmermann, Martin Faltys, Tobias M. Merz and Cristóbal Esteban and has published in prestigious journals such as Nucleic Acids Research, Nature Medicine and Nature Communications.

In The Last Decade

Max Horn

15 papers receiving 493 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
Max Horn Switzerland	9	211	207	72	63	60	17	507
Christopher S. Josef United States	8	216 1.0×	237 1.1×	105 1.5×	35 0.6×	99 1.6×	13	606
Marshall Nichols United States	14	201 1.0×	186 0.9×	68 0.9×	155 2.5×	60 1.0×	24	715
Annelaura Bach Nielsen Denmark	7	202 1.0×	144 0.7×	133 1.8×	42 0.7×	49 0.8×	18	527
Ke Lin China	11	137 0.6×	170 0.8×	125 1.7×	26 0.4×	64 1.1×	23	564
Thomas Desautels United States	9	388 1.8×	348 1.7×	24 0.3×	68 1.1×	82 1.4×	10	633
Simon Meyer Lauritsen Denmark	5	251 1.2×	132 0.6×	20 0.3×	82 1.3×	37 0.6×	7	418
Vicent Ribas Spain	15	171 0.8×	217 1.0×	50 0.7×	32 0.5×	134 2.2×	38	673
Piotr Jaroslaw Chmura Denmark	7	146 0.7×	109 0.5×	28 0.4×	38 0.6×	55 0.9×	14	335
Matthew D. Stanley United States	6	352 1.7×	381 1.8×	20 0.3×	85 1.3×	131 2.2×	8	681
Jens Schierbeck Denmark	10	112 0.5×	174 0.8×	37 0.5×	33 0.5×	61 1.0×	24	502

Countries citing papers authored by Max Horn

Since Specialization

Citations

This map shows the geographic impact of Max Horn'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 Max Horn with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Max Horn more than expected).

Fields of papers citing papers by Max Horn

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Max Horn. 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 Max Horn. The network helps show where Max Horn may publish in the future.

Co-authorship network of co-authors of Max Horn

This figure shows the co-authorship network connecting the top 25 collaborators of Max Horn. A scholar is included among the top collaborators of Max Horn 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 Max Horn. Max Horn 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:

17 of 17 papers shown

Gimpel, Andreas L., Dexiong Chen, Max Horn, et al.. (2025). Predicting sequence-specific amplification efficiency in multi-template PCR with deep learning. Nature Communications. 16(1). 9187–9187.

Littmann, Maria, Max Horn, Andreas Georgiou, et al.. (2025). Varicella-zoster virus reactivation and the risk of dementia. Nature Medicine. 31(12). 4172–4179. 3 indexed citations

Schwab, Patrick, Maria Littmann, Carolyn Buser‐Doepner, et al.. (2024). Recombinant zoster vaccine and reduced risk of dementia: matched‐cohort study using large‐scale electronic health records and machine learning methodology. Alzheimer s & Dementia. 20(S7). 2 indexed citations

Fan, Ke, Zechen Bai, Tianjun Xiao, et al.. (2024). Adaptive Slot Attention: Object Discovery with Dynamic Slot Number. 23062–23071.

Moor, Michael, Max Horn, Bastian Rieck, et al.. (2023). Predicting sepsis using deep learning across international sites: a retrospective development and validation study. EClinicalMedicine. 62. 102124–102124. 33 indexed citations

Zhao, Zixu, Max Horn, Tong He, et al.. (2023). Object-Centric Multiple Object Tracking. FreiDok plus (Universitätsbibliothek Freiburg). 16555–16565. 6 indexed citations

Fan, Ke, Zechen Bai, Tianjun Xiao, et al.. (2023). Unsupervised Open-Vocabulary Object Localization in Videos. FreiDok plus (Universitätsbibliothek Freiburg). 13701–13709. 3 indexed citations

Moor, Michael, Bastian Rieck, Max Horn, Catherine R. Jutzeler, & Karsten Borgwardt. (2021). Early Prediction of Sepsis in the ICU Using Machine Learning: A Systematic Review. Frontiers in Medicine. 8. 607952–607952. 106 indexed citations

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 →

10.

Weis, Caroline, Max Horn, Bastian Rieck, et al.. (2020). Topological and kernel-based microbial phenotype prediction from MALDI-TOF mass spectra. Bioinformatics. 36(Supplement_1). i30–i38. 22 indexed citations

11.

Horn, Max, Michael Moor, Christian Bock, Bastian Rieck, & Karsten Borgwardt. (2020). Set Functions for Time Series. Repository for Publications and Research Data (ETH Zurich). 119. 4353–4363. 2 indexed citations

12.

Moor, Michael, Bastian Rieck, Max Horn, Catherine R. Jutzeler, & Karsten Borgwardt. (2020). Early Prediction of Sepsis in the ICU using Machine Learning: A Systematic Review. medRxiv. 13 indexed citations

13.

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

14.

Moor, Michael, Max Horn, Bastian Rieck, Damian Roqueiro, & Karsten Borgwardt. (2019). Temporal Convolutional Networks and Dynamic Time Warping can Drastically Improve the Early Prediction of Sepsis.. arXiv (Cornell University). 13 indexed citations

15.

Moor, Michael, Max Horn, Bastian Rieck, Damian Roqueiro, & Karsten Borgwardt. (2019). Early Recognition of Sepsis with Gaussian Process Temporal Convolutional Networks and Dynamic Time Warping. 106. 2–26. 3 indexed citations

16.

Stank, Antonia, Daria B. Kokh, Max Horn, et al.. (2017). TRAPP webserver: predicting protein binding site flexibility and detecting transient binding pockets. Nucleic Acids Research. 45(W1). W325–W330. 38 indexed citations

17.

Ahlmann-Eltze, Constantin, Jan Gleixner, Carolin Schmelas, et al.. (2015). Backbone circularization of Bacillus subtilis family 11 xylanase increases its thermostability and its resistance against aggregation. Molecular BioSystems. 11(12). 3231–3243. 20 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.

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