Philipp Probst

2.0k total citations · 1 hit paper
17 papers, 878 citations indexed

About

Philipp Probst is a scholar working on Artificial Intelligence, Epidemiology and Emergency Medicine. According to data from OpenAlex, Philipp Probst has authored 17 papers receiving a total of 878 indexed citations (citations by other indexed papers that have themselves been cited), including 6 papers in Artificial Intelligence, 3 papers in Epidemiology and 3 papers in Emergency Medicine. Recurrent topics in Philipp Probst's work include Machine Learning and Data Classification (5 papers), Machine Learning and Algorithms (2 papers) and Traumatic Brain Injury and Neurovascular Disturbances (2 papers). Philipp Probst is often cited by papers focused on Machine Learning and Data Classification (5 papers), Machine Learning and Algorithms (2 papers) and Traumatic Brain Injury and Neurovascular Disturbances (2 papers). Philipp Probst collaborates with scholars based in Germany and Switzerland. Philipp Probst's co-authors include Anne‐Laure Boulesteix, Raphaël Couronné, Bernd Bischl, Roman Hornung, Moritz Herrmann, Vindi Jurinović, Nikolaus Plesnila, Katrin Rauen, Barbara Schäpers and Klaus Jahn and has published in prestigious journals such as Critical Care Medicine, BMC Bioinformatics and Journal of Machine Learning Research.

In The Last Decade

Philipp Probst

17 papers receiving 850 citations

Hit Papers

Random forest versus logistic regression: a large-scale b... 2018 2026 2020 2023 2018 100 200 300 400 500
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.

  1. Random forest versus logistic regression: a large-scale benchmark experiment
    2018 528 citations Raphaël Couronné, Philipp Probst et al. BMC Bioinformatics profile →

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Philipp Probst Germany 10 177 101 88 58 57 17 878
Jochen Kruppa Germany 22 281 1.6× 310 3.1× 137 1.6× 71 1.2× 82 1.4× 47 2.0k
Sanjay Kumar Yadav India 13 157 0.9× 60 0.6× 39 0.4× 176 3.0× 75 1.3× 111 1.1k
David E. Liston United States 6 322 1.8× 113 1.1× 148 1.7× 151 2.6× 146 2.6× 9 1.3k
Yasunobu Nohara Japan 12 155 0.9× 47 0.5× 120 1.4× 53 0.9× 91 1.6× 55 947
Aris Perperoglou United Kingdom 17 101 0.6× 73 0.7× 48 0.5× 92 1.6× 62 1.1× 37 934
Raphaël Couronné France 6 126 0.7× 50 0.5× 51 0.6× 59 1.0× 51 0.9× 8 652
Steven J. Rigatti United States 3 176 1.0× 150 1.5× 62 0.7× 47 0.8× 59 1.0× 12 980
Hyung‐Jin Yoon South Korea 18 125 0.7× 155 1.5× 138 1.6× 93 1.6× 81 1.4× 87 1.4k
Ljubomir Buturović United States 14 173 1.0× 257 2.5× 149 1.7× 45 0.8× 94 1.6× 33 1.3k
Tammy Jiang United States 12 102 0.6× 55 0.5× 82 0.9× 28 0.5× 71 1.2× 36 909

Countries citing papers authored by Philipp Probst

Since Specialization
Citations

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

Fields of papers citing papers by Philipp Probst

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Philipp Probst

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

All Works

17 of 17 papers shown
1.
Rauen, Katrin, Philipp Probst, Barbara Schäpers, et al.. (2020). Decompressive Craniectomy Is Associated With Good Quality of Life Up to 10 Years After Rehabilitation From Traumatic Brain Injury. Critical Care Medicine. 48(8). 1157–1164. 10 indexed citations
2.
Rauen, Katrin, Philipp Probst, Barbara Schäpers, et al.. (2020). Quality of life up to 10 years after traumatic brain injury: a cross-sectional analysis. Health and Quality of Life Outcomes. 18(1). 166–166. 44 indexed citations
3.
Fried, Roland, et al.. (2020). Analysis of Count Time Series [R package tscount version 1.4.3]. 1 indexed citations
4.
Herrmann, Moritz, Philipp Probst, Roman Hornung, Vindi Jurinović, & Anne‐Laure Boulesteix. (2020). Large-scale benchmark study of survival prediction methods using multi-omics data. Briefings in Bioinformatics. 22(3). 68 indexed citations
5.
Probst, Philipp. (2020). RF Variable Importance for Arbitrary Measures [R package varImp version 0.4]. 1 indexed citations
6.
Probst, Philipp, Anne‐Laure Boulesteix, & Bernd Bischl. (2019). Tunability: Importance of Hyperparameters of Machine Learning Algorithms. arXiv (Cornell University). 20(53). 1–32. 76 indexed citations
7.
Irlbeck, Thomas, Philipp Probst, Philipp M. Paprottka, et al.. (2019). Impact of pathologic body composition assessed by CT-based anthropometric measurements in adult patients with multiple trauma: a retrospective analysis. European Journal of Trauma and Emergency Surgery. 47(4). 1089–1103. 12 indexed citations
8.
Probst, Philipp, et al.. (2019). Modern Total Knee Arthroplasty (TKA): With Or Without a Tourniquet?. PubMed. 35. 336–340. 8 indexed citations
9.
Probst, Philipp. (2019). Tune Random Forest of the 'ranger' Package [R package tuneRanger version 0.5]. 1 indexed citations
10.
Probst, Philipp. (2019). Hyperparameters, tuning and meta-learning for random forest and other machine learning algorithms. Electronic Theses of LMU Munich (Ludwig-Maximilians-Universität München). 8 indexed citations
11.
Couronné, Raphaël, Philipp Probst, & Anne‐Laure Boulesteix. (2018). Random forest versus logistic regression: a large-scale benchmark experiment. BMC Bioinformatics. 19(1). 270–270. 528 indexed citations breakdown →
12.
Matthes, Sandhya, Gabriela Leuschner, Dieter Munker, et al.. (2018). Asthma features in severe COPD: Identifying treatable traits. Respiratory Medicine. 145. 89–94. 11 indexed citations
13.
Boulesteix, Anne‐Laure, et al.. (2018). Making complex prediction rules applicable for readers: Current practice in random forest literature and recommendations. Biometrical Journal. 61(5). 1314–1328. 13 indexed citations
14.
Probst, Philipp. (2017). Multilabel classification with R package mlr. The R Journal. 9(1). 352–369. 6 indexed citations
15.
Probst, Philipp, Quay Au, Giuseppe Casalicchio, Clemens Stachl, & Bernd Bischl. (2017). Multilabel Classification with R Package mlr. The R Journal. 9(1). 352–352. 13 indexed citations
16.
Probst, Philipp & Anne‐Laure Boulesteix. (2017). To tune or not to tune the number of trees in random forest?. Journal of Machine Learning Research. 18(181). 1–18. 77 indexed citations
17.
Fried, Roland, et al.. (2015). Analysis of Count Time Series. 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.

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