Thomas Tschoellitsch

757 total citations
18 papers, 188 citations indexed

About

Thomas Tschoellitsch is a scholar working on Epidemiology, Artificial Intelligence and Infectious Diseases. According to data from OpenAlex, Thomas Tschoellitsch has authored 18 papers receiving a total of 188 indexed citations (citations by other indexed papers that have themselves been cited), including 5 papers in Epidemiology, 5 papers in Artificial Intelligence and 4 papers in Infectious Diseases. Recurrent topics in Thomas Tschoellitsch's work include Sepsis Diagnosis and Treatment (5 papers), Machine Learning in Healthcare (4 papers) and Emergency and Acute Care Studies (4 papers). Thomas Tschoellitsch is often cited by papers focused on Sepsis Diagnosis and Treatment (5 papers), Machine Learning in Healthcare (4 papers) and Emergency and Acute Care Studies (4 papers). Thomas Tschoellitsch collaborates with scholars based in Austria, Croatia and United States. Thomas Tschoellitsch's co-authors include Jens Meier, Carl Böck, Karin Schwarzbauer, Sepp Hochreiter, Martin W. Dünser, Axel Hofmann, Günter Klambauer, Andreas Mitterecker, Michael F. Leahy and Kevin M. Trentino and has published in prestigious journals such as Scientific Reports, Thorax and Anesthesia & Analgesia.

In The Last Decade

Thomas Tschoellitsch

16 papers receiving 185 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Thomas Tschoellitsch Austria 8 46 43 39 36 35 18 188
Shengpu Tang United States 6 31 0.7× 83 1.9× 33 0.8× 2 0.1× 60 1.7× 10 230
E. Martínez Chamorro Spain 6 64 1.4× 15 0.3× 56 1.4× 2 0.1× 18 0.5× 20 175
Yuliya Pinevich United States 9 38 0.8× 26 0.6× 23 0.6× 45 1.3× 22 214
Nakeya Dewaswala United States 9 67 1.5× 21 0.5× 62 1.6× 26 0.7× 39 228
Elliott H Taylor United Kingdom 3 40 0.9× 28 0.7× 32 0.8× 27 0.8× 5 166
Mohamed Eltorki Canada 10 27 0.6× 42 1.0× 11 0.3× 3 0.1× 30 0.9× 43 303
Renata R. Almeida United States 11 26 0.6× 22 0.5× 119 3.1× 53 1.5× 22 348
Zainab Gandhi United States 11 67 1.5× 38 0.9× 89 2.3× 19 0.5× 28 323
Honoria Ocagli Italy 9 13 0.3× 41 1.0× 24 0.6× 49 1.4× 37 260
Kevin P. Seitz United States 8 48 1.0× 6 0.1× 10 0.3× 10 0.3× 88 2.5× 16 260

Countries citing papers authored by Thomas Tschoellitsch

Since Specialization
Citations

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

Fields of papers citing papers by Thomas Tschoellitsch

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Thomas Tschoellitsch

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

All Works

18 of 18 papers shown
1.
Tschoellitsch, Thomas, et al.. (2025). Acquired factor XIII deficiency in adult patients during ECMO: a prospective observational study. Scientific Reports. 15(1). 39110–39110.
2.
3.
Tschoellitsch, Thomas, Philipp Moser, Philipp Seidl, et al.. (2024). Machine learning prediction of unexpected readmission or death after discharge from intensive care: A retrospective cohort study. Journal of Clinical Anesthesia. 99. 111654–111654. 2 indexed citations
4.
Dünser, Martin W., Thomas Tschoellitsch, Markus Brückner, et al.. (2024). Emergency critical care: closing the gap between onset of critical illness and intensive care unit admission. Wiener klinische Wochenschrift. 136(23-24). 651–661. 4 indexed citations
5.
Tschoellitsch, Thomas, Philipp Moser, Philipp Seidl, et al.. (2024). Potential Predictors for Deterioration of Renal Function After Transfusion. Anesthesia & Analgesia. 138(3). 645–654. 1 indexed citations
6.
Tschoellitsch, Thomas, et al.. (2023). The Value of the First Clinical Impression as Assessed by 18 Observations in Patients Presenting to the Emergency Department. Journal of Clinical Medicine. 12(2). 724–724. 2 indexed citations
7.
Tschoellitsch, Thomas, Philipp Seidl, Carl Böck, et al.. (2023). Using emergency department triage for machine learning-based admission and mortality prediction. European Journal of Emergency Medicine. 30(6). 408–416. 10 indexed citations
8.
Dünser, Martin W., et al.. (2023). The value of a machine learning algorithm to predict adverse short-term outcome during resuscitation of patients with in-hospital cardiac arrest: a retrospective study. European Journal of Emergency Medicine. 30(4). 252–259. 4 indexed citations
9.
Tschoellitsch, Thomas, et al.. (2022). Machine learning-based prediction of massive perioperative allogeneic blood transfusion in cardiac surgery. European Journal of Anaesthesiology. 39(9). 766–773. 12 indexed citations
10.
Böck, Carl, et al.. (2022). Domain Shifts in Machine Learning Based Covid-19 Diagnosis From Blood Tests. Journal of Medical Systems. 46(5). 23–23. 23 indexed citations
11.
Böck, Carl, et al.. (2022). Lifting Hospital Electronic Health Record Data Treasures: Challenges and Opportunities. JMIR Medical Informatics. 10(10). e38557–e38557. 6 indexed citations
12.
Kovács, Péter, Carl Böck, Thomas Tschoellitsch, Mario Huemer, & Jens Meier. (2022). Diagnostic quality assessment for low-dimensional ECG representations. Computers in Biology and Medicine. 150. 106086–106086. 2 indexed citations
13.
Meier, Jens & Thomas Tschoellitsch. (2022). Artificial Intelligence and Machine Learning in Patient Blood Management: A Scoping Review. Anesthesia & Analgesia. 135(3). 524–531. 22 indexed citations
14.
Heldt, Sven, Thomas Tschoellitsch, Bernhard Kaiser, et al.. (2021). qSOFA score poorly predicts critical progression in COVID-19 patients. Wiener Medizinische Wochenschrift. 172(9-10). 211–219. 8 indexed citations
15.
Böck, Carl, et al.. (2021). Machine Learning Based Color Classification by Means of Visually Evoked Potentials. Applied Sciences. 11(24). 11882–11882. 3 indexed citations
16.
Jirak, Peter, Robert Larbig, Daniel Dankl, et al.. (2020). Myocardial Injury in Severe COVID-19 is Similar to Pneumonias of Other Origin: Results from a Multicentre Study. ESC Heart Failure. 8(1). 37–46. 30 indexed citations
17.
Mitterecker, Andreas, Axel Hofmann, Kevin M. Trentino, et al.. (2020). Machine learning–based prediction of transfusion. Transfusion. 60(9). 1977–1986. 32 indexed citations
18.
Tschoellitsch, Thomas, Martin W. Dünser, Carl Böck, Karin Schwarzbauer, & Jens Meier. (2020). Machine Learning Prediction of SARS-CoV-2 Polymerase Chain Reaction Results with Routine Blood Tests. Laboratory Medicine. 52(2). 146–149. 27 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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2026