Barbaros Yet

1.1k total citations
41 papers, 740 citations indexed

About

Barbaros Yet is a scholar working on Artificial Intelligence, Management Science and Operations Research and Statistics, Probability and Uncertainty. According to data from OpenAlex, Barbaros Yet has authored 41 papers receiving a total of 740 indexed citations (citations by other indexed papers that have themselves been cited), including 19 papers in Artificial Intelligence, 9 papers in Management Science and Operations Research and 8 papers in Statistics, Probability and Uncertainty. Recurrent topics in Barbaros Yet's work include Bayesian Modeling and Causal Inference (18 papers), Trauma and Emergency Care Studies (6 papers) and Vascular Procedures and Complications (5 papers). Barbaros Yet is often cited by papers focused on Bayesian Modeling and Causal Inference (18 papers), Trauma and Emergency Care Studies (6 papers) and Vascular Procedures and Complications (5 papers). Barbaros Yet collaborates with scholars based in United Kingdom, Türkiye and United States. Barbaros Yet's co-authors include Zane Perkins, William Marsh, Nigel Tai, Norman Fenton, Martin Neil, Anthony C. Constantinou, Todd E. Rasmussen, Keith Shepherd, Simon Glasgow and Eike Luedeling and has published in prestigious journals such as PLoS ONE, Annals of Surgery and Expert Systems with Applications.

In The Last Decade

Barbaros Yet

40 papers receiving 725 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Barbaros Yet United Kingdom 15 175 150 128 127 122 41 740
William Marsh United Kingdom 20 123 0.7× 279 1.9× 118 0.9× 98 0.8× 98 0.8× 71 1.2k
Guilan Kong China 20 82 0.5× 360 2.4× 54 0.4× 48 0.4× 117 1.0× 57 1.1k
Liam O’Neill United States 18 152 0.9× 26 0.2× 119 0.9× 68 0.5× 463 3.8× 41 1.3k
Petar Momčilović United States 15 200 1.1× 82 0.5× 95 0.7× 43 0.3× 166 1.4× 49 1.0k
Hari Balasubramanian United States 19 157 0.9× 44 0.3× 249 1.9× 51 0.4× 169 1.4× 52 1.3k
Lisa M. Maillart United States 17 113 0.6× 52 0.3× 19 0.1× 71 0.6× 91 0.7× 56 1.4k
Nitin Patel United States 11 162 0.9× 76 0.5× 56 0.4× 72 0.6× 38 0.3× 28 671
Kam-Fung Cheung Australia 11 77 0.4× 30 0.2× 28 0.2× 88 0.7× 12 0.1× 19 540
Vincent Augusto France 20 131 0.7× 109 0.7× 107 0.8× 11 0.1× 224 1.8× 91 1.2k
Stefan Steiner Canada 15 94 0.5× 36 0.2× 13 0.1× 56 0.4× 89 0.7× 44 656

Countries citing papers authored by Barbaros Yet

Since Specialization
Citations

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

Fields of papers citing papers by Barbaros Yet

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Barbaros Yet

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

All Works

20 of 20 papers shown
1.
Wohlgemut, Jared M., et al.. (2024). Presenting predictions and performance of probabilistic models for clinical decision support in trauma care. International Journal of Medical Informatics. 194. 105702–105702. 1 indexed citations
2.
Yet, Barbaros, et al.. (2024). Secondary data analysis using Evidence-Based Bayesian Networks with an application to investigate the determinants of childhood stunting. Expert Systems with Applications. 256. 124940–124940. 1 indexed citations
3.
Şakar, Ceren Tuncer, et al.. (2024). Multicriteria decision support under uncertainty: combining outranking methods with Bayesian networks. Annals of Operations Research. 355(3). 2971–2998. 3 indexed citations
4.
5.
6.
Yet, Barbaros, et al.. (2023). Assessing Serious Spinal Pathology Using Bayesian Network Decision Support: Development and Validation Study. JMIR Formative Research. 7. e44187–e44187. 1 indexed citations
7.
Şakar, Ceren Tuncer, et al.. (2022). Reducing the question burden of patient reported outcome measures using Bayesian networks. Journal of Biomedical Informatics. 135. 104230–104230. 3 indexed citations
8.
Yet, Barbaros, et al.. (2021). Effectiveness of intubation devices in patients with cervical spine immobilisation: a systematic review and network meta-analysis. British Journal of Anaesthesia. 126(5). 1055–1066. 26 indexed citations
9.
Yet, Barbaros, Christine Lamanna, Keith Shepherd, & Todd S. Rosenstock. (2020). Evidence-based investment selection: Prioritizing agricultural development investments under climatic and socio-political risk using Bayesian networks. PLoS ONE. 15(6). e0234213–e0234213. 13 indexed citations
10.
Yet, Barbaros, et al.. (2020). A Bayesian Network Decision Support Tool for Low Back Pain Using a RAND Appropriateness Procedure: Proposal and Internal Pilot Study. JMIR Research Protocols. 10(1). e21804–e21804. 5 indexed citations
11.
Yet, Barbaros, et al.. (2019). Building Bayesian networks based on DEMATEL for multiple criteria decision problems: A supplier selection case study. Expert Systems with Applications. 134. 234–248. 80 indexed citations
12.
Yet, Barbaros, et al.. (2019). Football Analytics using Bayesian Networks: the FutBA Model. Pamukkale University Journal of Engineering Sciences. 25(1). 121–131.
13.
Yet, Barbaros, et al.. (2018). An improved method for solving Hybrid Influence Diagrams. International Journal of Approximate Reasoning. 95. 93–112. 6 indexed citations
14.
Yet, Barbaros, et al.. (2017). The role of splenic angioembolization as an adjunct to nonoperative management of blunt splenic injuries: A systematic review and meta-analysis. The Journal of Trauma: Injury, Infection, and Critical Care. 83(5). 934–943. 58 indexed citations
15.
Fenton, Norman, Martin Neil, David A. Lagnado, et al.. (2016). How to model mutually exclusive events based on independent causal pathways in Bayesian network models. Knowledge-Based Systems. 113. 39–50. 16 indexed citations
16.
Perkins, Zane, Barbaros Yet, & Simon Glasgow. (2015). Meta-Analysis of Prognostic Factors for Amputation Following Surgical Repair of Lower Extremity Vascular Trauma. Journal of Vascular Surgery. 62(1). 265–265. 2 indexed citations
17.
Constantinou, Anthony C., Barbaros Yet, Norman Fenton, Martin Neil, & William Marsh. (2015). Value of information analysis for interventional and counterfactual Bayesian networks in forensic medical sciences. Artificial Intelligence in Medicine. 66. 41–52. 14 indexed citations
18.
Perkins, Zane, Barbaros Yet, Simon Glasgow, et al.. (2014). PS170. Prognostic Factors for Amputation Following Surgical Repair of Lower Extremity Vascular Trauma: A Systematic Review and Meta-Analysis of Observational Studies. Journal of Vascular Surgery. 59(6). 75S–75S. 1 indexed citations
19.
Yet, Barbaros, Zane Perkins, Todd E. Rasmussen, Nigel Tai, & William Marsh. (2014). Combining data and meta-analysis to build Bayesian networks for clinical decision support. Journal of Biomedical Informatics. 52. 373–385. 27 indexed citations
20.
Yet, Barbaros, Zane Perkins, Norman Fenton, Nigel Tai, & William Marsh. (2013). Not just data: A method for improving prediction with knowledge. Journal of Biomedical Informatics. 48. 28–37. 48 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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