Badih Ghattas

1.7k total citations
57 papers, 1.1k citations indexed

About

Badih Ghattas is a scholar working on Artificial Intelligence, Cardiology and Cardiovascular Medicine and Radiology, Nuclear Medicine and Imaging. According to data from OpenAlex, Badih Ghattas has authored 57 papers receiving a total of 1.1k indexed citations (citations by other indexed papers that have themselves been cited), including 15 papers in Artificial Intelligence, 8 papers in Cardiology and Cardiovascular Medicine and 7 papers in Radiology, Nuclear Medicine and Imaging. Recurrent topics in Badih Ghattas's work include Multiple Sclerosis Research Studies (6 papers), Statistical Methods and Inference (5 papers) and Bayesian Methods and Mixture Models (4 papers). Badih Ghattas is often cited by papers focused on Multiple Sclerosis Research Studies (6 papers), Statistical Methods and Inference (5 papers) and Bayesian Methods and Mixture Models (4 papers). Badih Ghattas collaborates with scholars based in France, Uruguay and United States. Badih Ghattas's co-authors include Carolina Crisci, G. Perera, David Bendahan, Sylviane Confort‐Gouny, David Nérini, Laurent Boyer, Patrick J. Cozzone, J. P. Mattei, Pierre Michel and Ricardo Fraiman and has published in prestigious journals such as SHILAP Revista de lepidopterología, Bioinformatics and PLoS ONE.

In The Last Decade

Badih Ghattas

54 papers receiving 1.1k citations

Peers

Badih Ghattas

RNMI

GENET

Ping Huang China

Ting Shen China

Sun China

Øivind Skare Norway

Xiaofei Hu China

Yi‐An Chen Taiwan

Wenlong Tang China

Silke Szymczak Germany

Edsel A. Peña United States

Linyuan Zhang China

Ping Huang China

Badih Ghattas

58 ×0.3

170 ×1.1

RNMI

326 ×2.2

79 ×0.8

GENET

58 ×0.6

Citations per year, relative to Badih Ghattas Badih Ghattas (= 1×) peers Ping Huang

Countries citing papers authored by Badih Ghattas

Since Specialization

Citations

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

Fields of papers citing papers by Badih Ghattas

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Badih Ghattas

This figure shows the co-authorship network connecting the top 25 collaborators of Badih Ghattas. A scholar is included among the top collaborators of Badih Ghattas 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 Badih Ghattas. Badih Ghattas 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

Ghattas, Badih, et al.. (2025). Clustering Approaches for Mixed‐Type Data: A Comparative Study. Journal of Probability and Statistics. 2025(1). 1 indexed citations

Cugliari, Jairo, et al.. (2024). Textual data for electricity load forecasting. Quality and Reliability Engineering International. 40(8). 4187–4208.

Ghattas, Badih, et al.. (2023). Finding the best trade-off between performance and interpretability in predicting hospital length of stay using structured and unstructured data. PLoS ONE. 18(11). e0289795–e0289795. 5 indexed citations

Ghattas, Badih, et al.. (2023). Machine Learning Alternatives to Response Surface Models. Mathematics. 11(15). 3406–3406. 13 indexed citations

Bartoli, Axel, Farouk Tradi, Badih Ghattas, et al.. (2022). Automatic Deep-Learning Segmentation of Epicardial Adipose Tissue from Low-Dose Chest CT and Prognosis Impact on COVID-19. Cells. 11(6). 1034–1034. 7 indexed citations

Ghattas, Badih, et al.. (2021). Inferring linear and nonlinear Interaction networks using neighborhood support vector machines. arXiv (Cornell University). 1–6. 2 indexed citations

Fraiman, Ricardo, et al.. (2018). Multivariate and functional robust fusion methods for structured Big Data. Journal of Multivariate Analysis. 170. 149–161. 5 indexed citations

Michel, Pierre, Karine Baumstarck, Anderson Loundou, et al.. (2018). Computerized adaptive testing with decision regression trees: an alternative to item response theory for quality of life measurement in multiple sclerosis. Patient Preference and Adherence. Volume 12. 1043–1053. 8 indexed citations

Michel, Pierre, Karine Baumstarck, Christophe Lançon, et al.. (2017). Modernizing quality of life assessment: development of a multidimensional computerized adaptive questionnaire for patients with schizophrenia. Quality of Life Research. 27(4). 1041–1054. 13 indexed citations

10.

Michel, Pierre, Zeinab Hamidou, Karine Baumstarck, et al.. (2017). Clustering based on unsupervised binary trees to define subgroups of cancer patients according to symptom severity in cancer. Quality of Life Research. 27(2). 555–565. 1 indexed citations

11.

Michel, Pierre, Karine Baumstarck, Badih Ghattas, et al.. (2016). A Multidimensional Computerized Adaptive Short-Form Quality of Life Questionnaire Developed and Validated for Multiple Sclerosis. Medicine. 95(14). e3068–e3068. 15 indexed citations

12.

Michel, Pierre, Karine Baumstarck, Badih Ghattas, et al.. (2016). A Multidimensional Computerized Adaptive Short-Form Quality of Life ă Questionnaire Developed and Validated for Multiple Sclerosis The ă MusiQoL-MCAT. HAL (Le Centre pour la Communication Scientifique Directe). 3 indexed citations

13.

Pons, Maite, et al.. (2015). MACHINE LEARNING PROCEDURES: AN APPLICATION TO BY-CATCH DATA OF THE MARINE TURTLES CARETTA CARETTA IN THE SOUTHWESTERN ATLANTIC OCEAN. 1 indexed citations

14.

Boyer, Laurent, Karine Baumstarck, Pierre Michel, et al.. (2013). Statistical challenges of quality of life and cancer: new avenues for future research. Expert Review of Pharmacoeconomics & Outcomes Research. 14(1). 19–22. 7 indexed citations

15.

Boucékine, Mohamed, Anderson Loundou, Karine Baumstarck, et al.. (2013). Using the random forest method to detect a response shift in the quality of life of multiple sclerosis patients: a cohort study. BMC Medical Research Methodology. 13(1). 20–20. 29 indexed citations

16.

Ghattas, Badih & Anis Ben Ishak. (2008). Sélection de variables pour la classification binaire en grande dimension: comparaisons et application aux domées de biopuces. 149(3). 43–66. 11 indexed citations

17.

Lopez, Fabrice, Samuel Granjeaud, Takeshi Ara, Badih Ghattas, & Daniel Gautheret. (2006). The disparate nature of “intergenic” polyadenylation sites. RNA. 12(10). 1794–1801. 24 indexed citations

18.

Martín, David, Badih Ghattas, & Denis Thieffry. (2004). Prédire la transcription à partir des séquences génomique. médecine/sciences. 20(11). 1036–1040.

19.

Ghattas, Badih. (1999). Previsions des pics d'ozone par arbres de regression, simples et agreges par bootstrap. Revue de Statistique Appliquée. 47(2). 61–80. 7 indexed citations

20.

Ghattas, Badih. (1999). Agregation d'arbres de classification. RePEc: Research Papers in Economics. 48(2). 85–98. 2 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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