Eva Iglesias

2.7k total citations
54 papers, 757 citations indexed

About

Eva Iglesias is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Epidemiology. According to data from OpenAlex, Eva Iglesias has authored 54 papers receiving a total of 757 indexed citations (citations by other indexed papers that have themselves been cited), including 23 papers in Artificial Intelligence, 9 papers in Computer Vision and Pattern Recognition and 9 papers in Epidemiology. Recurrent topics in Eva Iglesias's work include Text and Document Classification Technologies (13 papers), Topic Modeling (8 papers) and Inflammatory Bowel Disease (7 papers). Eva Iglesias is often cited by papers focused on Text and Document Classification Technologies (13 papers), Topic Modeling (8 papers) and Inflammatory Bowel Disease (7 papers). Eva Iglesias collaborates with scholars based in Spain, United States and Mexico. Eva Iglesias's co-authors include L. Borrajo, Fernando Díaz, José R. Méndez, Juan M. Corchado, Florentino Fdez‐Riverola, Javier P. Gisbert, Marta Calvo, Esther García-Planella, Valentí Puig‐Diví and Beatriz Lobo and has published in prestigious journals such as SHILAP Revista de lepidopterología, PLoS ONE and Expert Systems with Applications.

In The Last Decade

Eva Iglesias

46 papers receiving 737 citations

Peers

Eva Iglesias

AI

EPIDE

GENET

IS

SURGE

Ji Hyung Hong South Korea

David S. Matheson Canada

Volker Ziegler Germany

Dianbo Liu United States

Vincenzo De Angelis Italy

Agnieszka Oniśko United States

Faisal Azam Saudi Arabia

Hosik Choi South Korea

Mitsuaki Akiyama Japan

Ji Hyung Hong South Korea

Eva Iglesias

127 ×0.5

AI

46 ×0.2

EPIDE

33 ×0.2

GENET

91 ×0.8

IS

150 ×1.9

SURGE

Citations per year, relative to Eva Iglesias Eva Iglesias (= 1×) peers Ji Hyung Hong

Countries citing papers authored by Eva Iglesias

Since Specialization

Citations

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

Fields of papers citing papers by Eva Iglesias

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Eva Iglesias

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

1.

Rodríguez–Lago, Iago, Fiorella Cañete, Claudia Herrera‐deGuise, et al.. (2024). Combination of granulocyte–monocyte apheresis and tofacitinib: Multicentre and retrospective study. Gastroenterología y Hepatología. 47(7). 727–733.

2.

Rodríguez–Lago, Iago, Fiorella Cañete, Claudia Herrera‐deGuise, et al.. (2024). Combination of granulocyte–monocyte apheresis and tofacitinib: Multicentre and retrospective study. Gastroenterología y Hepatología (English Edition). 47(7). 727–733.

3.

Salazar, Juliana, Esther García-Planella, A Fernández-Clotet, et al.. (2024). Genetic biomarkers of methotrexate response and safety in Crohn's disease: Data from the Spanish ENEIDA registry. British Journal of Clinical Pharmacology. 90(5). 1301–1311. 3 indexed citations

4.

Iglesias, Eva, et al.. (2024). New Metrics and Dataset for Biological Development Video Generation. ACM Transactions on Multimedia Computing Communications and Applications. 21(1). 1–23.

5.

Iglesias, Eva, et al.. (2023). Temporal Development GAN (TD-GAN): Crafting More Accurate Image Sequences of Biological Development. Information. 15(1). 12–12.

6.

Gros, Beatriz, et al.. (2022). Impact ofClostridioides difficileinfection in patients admitted with ulcerative colitis. Scandinavian Journal of Gastroenterology. 58(3). 232–239. 7 indexed citations

7.

Iglesias, Eva, et al.. (2022). A survey on deep learning applied to medical images: from simple artificial neural networks to generative models. Neural Computing and Applications. 35(3). 2291–2323. 92 indexed citations

8.

Iglesias, Eva, et al.. (2020). LDA filter: A Latent Dirichlet Allocation preprocess method for Weka. PLoS ONE. 15(11). e0241701–e0241701. 9 indexed citations

9.

Gal, Beatriz, et al.. (2018). Evaluation of participatory teaching methods in undergraduate medical students’ learning along the first academic courses. PLoS ONE. 13(1). e0190173–e0190173. 15 indexed citations

10.

Iglesias, Eva, M.C. Pastor, Jaume Barallat, et al.. (2016). Inflammation and oxidation: do they improve after kidney transplantation? Relationship with mortality after transplantation. International Urology and Nephrology. 49(3). 533–540. 7 indexed citations

11.

Borrajo, L., et al.. (2016). Improving the text classification using clustering and a novel HMM to reduce the dimensionality. Computer Methods and Programs in Biomedicine. 136. 119–130. 14 indexed citations

12.

Rodríguez, Manuel de Buenaga, Diego Gachet Páez, Manuel Jesús Maña López, et al.. (2015). IPHealth: Plataforma inteligente basada en open, linked y big data para la toma de decisiones y aprendizaje en el ámbito de la salud. Procesamiento del lenguaje natural. 55(55). 161–164. 1 indexed citations

13.

Coronado, Pluvio, Rafael Sánchez‐Borrego, Miguel A. Ruíz, et al.. (2015). Psychometric attributes of the Cervantes short-form questionnaire for measuring health-related quality of life in menopausal women. Maturitas. 84. 55–62. 18 indexed citations

14.

Fernández‐Bañares, Fernando, Azucena Salas, Belén Beltrán, et al.. (2013). Impact of Current Smoking on the Clinical Course of Microscopic Colitis. Inflammatory Bowel Diseases. 19(7). 1470–1476. 49 indexed citations

15.

Asensio, Juan A., et al.. (2012). Argon Beam Coagulator: An Effective Adjunct to Stapled Pulmonary Tractotomy to Control Hemorrhage in Penetrating Pulmonary Injuries. Journal of the American College of Surgeons. 214(3). e9–e12. 3 indexed citations

16.

Pérez-Alonso, Alejandro J., et al.. (2012). Femoral vessel injuries; high mortality and low morbidity injuries. European Journal of Trauma and Emergency Surgery. 38(4). 359–371. 2 indexed citations

17.

Tuan, Vu Ngoc, et al.. (2012). Shank vessel injuries: the forgotten vascular injuries. European Journal of Trauma and Emergency Surgery. 38(4). 393–401. 5 indexed citations

18.

Pérez-Alonso, Alejandro J., et al.. (2012). Iliac vessel injuries: difficult injuries and difficult management problems. European Journal of Trauma and Emergency Surgery. 38(4). 347–357. 5 indexed citations

19.

Chaparro, María, Eva Iglesias, Julián Panés, et al.. (2011). Infliximab salvage therapy after failure of ciclosporin in corticosteroid‐refractory ulcerative colitis: a multicentre study. Alimentary Pharmacology & Therapeutics. 35(2). 275–283. 82 indexed citations

20.

Méndez, José R., Eva Iglesias, Florentino Fdez‐Riverola, Fernando Díaz, & Juan M. Corchado. (2005). Analyzing the Impact of Corpus Preprocessing on AntiSpam Filtering Software. Research in computing science. 17. 129–138. 4 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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