Julia E. Vogt

1.8k total citations
52 papers, 930 citations indexed

About

Julia E. Vogt is a scholar working on Artificial Intelligence, Surgery and Cardiology and Cardiovascular Medicine. According to data from OpenAlex, Julia E. Vogt has authored 52 papers receiving a total of 930 indexed citations (citations by other indexed papers that have themselves been cited), including 14 papers in Artificial Intelligence, 7 papers in Surgery and 6 papers in Cardiology and Cardiovascular Medicine. Recurrent topics in Julia E. Vogt's work include Machine Learning in Healthcare (6 papers), Hepatitis C virus research (4 papers) and Cytokine Signaling Pathways and Interactions (4 papers). Julia E. Vogt is often cited by papers focused on Machine Learning in Healthcare (6 papers), Hepatitis C virus research (4 papers) and Cytokine Signaling Pathways and Interactions (4 papers). Julia E. Vogt collaborates with scholars based in Switzerland, Germany and United States. Julia E. Vogt's co-authors include Ričards Marcinkevičs, Volker Röth, Michael T. Dill, Markus H. Heim, Luigi Terracciano, Sven Wellmann, Pierre–Yves Bochud, Stéphanie Bibert, Andreas Papassotiropoulos and François H.T. Duong and has published in prestigious journals such as Journal of Clinical Investigation, Gastroenterology and PLoS ONE.

In The Last Decade

Julia E. Vogt

46 papers receiving 909 citations

Peers

Julia E. Vogt

HEPAT

EPIDE

RNMI

Mauro Giuffrè Italy

Hyung‐Chul Lee South Korea

Zheng Ye China

Qingfeng Sun China

Sven Van Poucke Belgium

Qiushi Chen United States

Abraham Pouliakis Greece

Fei Gao China

Yutian Chong China

Gene Pennello United States

Mauro Giuffrè Italy

Julia E. Vogt

207 ×0.9

HEPAT

334 ×1.5

EPIDE

166 ×1.2

206 ×2.0

107 ×1.1

RNMI

Citations per year, relative to Julia E. Vogt Julia E. Vogt (= 1×) peers Mauro Giuffrè

Countries citing papers authored by Julia E. Vogt

Since Specialization

Citations

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

Fields of papers citing papers by Julia E. Vogt

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Julia E. Vogt. 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 Julia E. Vogt. The network helps show where Julia E. Vogt may publish in the future.

Co-authorship network of co-authors of Julia E. Vogt

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

All Works

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20 of 20 papers shown

Urteaga, Iñigo, George Hripcsak, Pierre Elias, et al.. (2025). AI as an intervention: improving clinical outcomes relies on a causal approach to AI development and validation. Journal of the American Medical Informatics Association. 32(3). 589–594. 4 indexed citations

Vogt, Julia E. & Michael Föller. (2025). Regulation of αKlotho. Cellular Physiology and Biochemistry. 59(4). 511–524.

Sokol, Kacper, James C. Fackler, & Julia E. Vogt. (2025). Artificial intelligence should genuinely support clinical reasoning and decision making to bridge the translational gap. npj Digital Medicine. 8(1). 345–345. 8 indexed citations

Adil, Md., Maximilian Schmidt, Julia E. Vogt, et al.. (2024). Mitigating Dissolution to Enhance the Performance of Pillar[5]quinone in Sodium Batteries. Batteries & Supercaps. 7(12). 4 indexed citations

Wellmann, Sven, et al.. (2024). Deep Learning Based Prediction of Pulmonary Hypertension in Newborns Using Echocardiograms. International Journal of Computer Vision. 132(7). 2567–2584. 7 indexed citations

Ramírez, Elisa, Rubén Casado-Arroyo, José Luís Merino, et al.. (2024). The art of selecting the ECG input in neural networks to classify heart diseases: a dual focus on maximizing information and reducing redundancy. Frontiers in Physiology. 15. 1452829–1452829.

Schumacher, Kai, et al.. (2023). Validating the early phototherapy prediction tool across cohorts. Frontiers in Pediatrics. 11. 1229462–1229462. 1 indexed citations

Marcinkevičs, Ričards, et al.. (2023). Interpretable and intervenable ultrasonography-based machine learning models for pediatric appendicitis. Medical Image Analysis. 91. 103042–103042. 19 indexed citations

Schuurmans, Macé M., Michał Muszyński, Xiang Li, et al.. (2023). Multimodal Remote Home Monitoring of Lung Transplant Recipients during COVID-19 Vaccinations: Usability Pilot Study of the COVIDA Desk Incorporating Wearable Devices. Medicina. 59(3). 617–617. 2 indexed citations

10.

Marx, Alexander, Francesco Di Stefano, Heike Leutheuser, et al.. (2023). Blood glucose forecasting from temporal and static information in children with T1D. Frontiers in Pediatrics. 11. 1296904–1296904. 4 indexed citations

11.

Feger, Martina, Jörg Strotmann, Miriam Hoene, et al.. (2023). Endothelin receptor B-deficient mice are protected from high-fat diet-induced metabolic syndrome. Molecular Metabolism. 80. 101868–101868. 2 indexed citations

12.

Vogt, Julia E., et al.. (2023). This Reads Like That: Deep Learning for Interpretable Natural Language Processing. 14067–14076. 1 indexed citations

13.

Veerbeek, Janne M., et al.. (2022). Classification of functional and non-functional arm use by inertial measurement units in individuals with upper limb impairment after stroke. Frontiers in Physiology. 13. 952757–952757. 12 indexed citations

14.

Marcinkevičs, Ričards, et al.. (2021). A Deep Variational Approach to Clustering Survival Data. Repository for Publications and Research Data (ETH Zurich). 4 indexed citations

15.

Jacobsen, Marc, et al.. (2021). Machine Learning Algorithms Evaluate Immune Response to Novel Mycobacterium tuberculosis Antigens for Diagnosis of Tuberculosis. Frontiers in Cellular and Infection Microbiology. 10. 594030–594030. 14 indexed citations

16.

Koch, Gilbert, et al.. (2019). Enhanced early prediction of clinically relevant neonatal hyperbilirubinemia with machine learning. Pediatric Research. 86(1). 122–127. 35 indexed citations

17.

Battegay, Manuel, et al.. (2018). Introduction to Machine Learning in Digital Healthcare Epidemiology. Infection Control and Hospital Epidemiology. 39(12). 1457–1462. 56 indexed citations

18.

Vogt, Julia E. & Volker Röth. (2012). A complete analysis of the l 1,p group-lasso. International Conference on Machine Learning. 1091–1098. 4 indexed citations

19.

Vogt, Julia E., Sandhya Prabhakaran, Thomas J. Fuchs, & Volker Röth. (2010). The Translation-invariant Wishart-Dirichlet Process for Clustering Distance Data. International Conference on Machine Learning. 1111–1118. 8 indexed citations

20.

Dill, Michael T., François H.T. Duong, Julia E. Vogt, et al.. (2010). Interferon-Induced Gene Expression Is a Stronger Predictor of Treatment Response Than IL28B Genotype in Patients With Hepatitis C. Gastroenterology. 140(3). 1021–1031.e10. 201 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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