Iván Contreras

1.4k total citations · 1 hit paper
46 papers, 875 citations indexed

About

Iván Contreras is a scholar working on Endocrinology, Diabetes and Metabolism, Artificial Intelligence and Genetics. According to data from OpenAlex, Iván Contreras has authored 46 papers receiving a total of 875 indexed citations (citations by other indexed papers that have themselves been cited), including 27 papers in Endocrinology, Diabetes and Metabolism, 14 papers in Artificial Intelligence and 11 papers in Genetics. Recurrent topics in Iván Contreras's work include Diabetes Management and Research (26 papers), Diabetes and associated disorders (11 papers) and Pancreatic function and diabetes (8 papers). Iván Contreras is often cited by papers focused on Diabetes Management and Research (26 papers), Diabetes and associated disorders (11 papers) and Pancreatic function and diabetes (8 papers). Iván Contreras collaborates with scholars based in Spain, Brazil and United Kingdom. Iván Contreras's co-authors include Josep Vehı́, Arthur Bertachi, Silvia Oviedo, Marga Giménez, Ignacio Conget, Martina Vettoretti, R. Visentin, Carmen Quirós, Clara Viñals and J. Ignacio Hidalgo and has published in prestigious journals such as SHILAP Revista de lepidopterología, PLoS ONE and Scientific Reports.

In The Last Decade

Iván Contreras

42 papers receiving 848 citations

Hit Papers

align trajectories

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.

Artificial Intelligence for Diabetes Management and Decision Support: Literature Review

2018 334 citations Iván Contreras, Josep Vehı́ profile →

Peers

Iván Contreras

EDM

HIM

GENET

SURGE

Taiyu Zhu United Kingdom

Konstantia Zarkogianni Greece

Cindy Marling United States

M. Elena Hernando Spain

Eleni I. Georga Greece

Gema García-Sáez Spain

Giordano Lanzola Italy

Pasquale De Cata Italy

Tibor Deutsch Hungary

Valentina Tibollo Italy

Taiyu Zhu United Kingdom

Iván Contreras

620 ×1.3

EDM

323 ×1.3

HIM

232 ×1.0

207 ×1.0

GENET

166 ×1.2

SURGE

Citations per year, relative to Iván Contreras Iván Contreras (= 1×) peers Taiyu Zhu

Countries citing papers authored by Iván Contreras

Since Specialization

Citations

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

Fields of papers citing papers by Iván Contreras

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Iván Contreras

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

Vehı́, Josep, et al.. (2025). Generative artificial intelligence in diabetes healthcare. iScience. 28(8). 113051–113051. 2 indexed citations

Contreras, Iván, et al.. (2025). Explainable cluster-based learning for prediction of postprandial glycemic events and insulin dose optimization in type 1 diabetes. PLOS Digital Health. 4(9). e0000996–e0000996.

Zhu, Taiyu, et al.. (2024). An automatic deep reinforcement learning bolus calculator for automated insulin delivery systems. Scientific Reports. 14(1). 15245–15245. 6 indexed citations

Contreras, Iván, et al.. (2024). The Effects of Environmental Factors on General Human Health: A Scoping Review. Healthcare. 12(21). 2123–2123. 7 indexed citations

Contreras, Iván, et al.. (2024). An ensemble machine learning approach for the detection of unannounced meals to enhance postprandial glucose control. Computers in Biology and Medicine. 171. 108154–108154. 4 indexed citations

Contreras, Iván, et al.. (2024). Generative deep learning for the development of a type 1 diabetes simulator. SHILAP Revista de lepidopterología. 4(1). 51–51. 5 indexed citations

Contreras, Iván, et al.. (2024). The Impact of Missing Continuous Blood Glucose Samples on Machine Learning Models for Predicting Postprandial Hypoglycemia: An Experimental Analysis. Mathematics. 12(10). 1567–1567. 6 indexed citations

Contreras, Iván, et al.. (2023). Individualized Prediction of Blood Glucose Outcomes Using Compositional Data Analysis. Mathematics. 11(21). 4517–4517.

Contreras, Iván, Judith Navarro‐Otano, Ignasi Rodríguez‐Pintó, et al.. (2023). Optimizing Noninvasive Vagus Nerve Stimulation for Systemic Lupus Erythematosus: Protocol for a Multicenter Randomized Controlled Trial. JMIR Research Protocols. 12. e48387–e48387. 2 indexed citations

10.

Contreras, Iván, et al.. (2023). Active Labeling Correction of Mealtimes and the Appearance of Types of Carbohydrates in Type 1 Diabetes Information Records. Mathematics. 11(19). 4050–4050. 1 indexed citations

11.

Contreras, Iván, Marga Giménez, Ignacio Conget, et al.. (2023). Validation of a Probabilistic Prediction Model for Patients with Type 1 Diabetes Using Compositional Data Analysis. Mathematics. 11(5). 1241–1241. 1 indexed citations

12.

Contreras, Iván, et al.. (2022). Conditional Synthesis of Blood Glucose Profiles for T1D Patients Using Deep Generative Models. Mathematics. 10(20). 3741–3741. 9 indexed citations

13.

Contreras, Iván, Arthur Bertachi, Ignacio Conget, et al.. (2022). A Machine Learning Approach to Minimize Nocturnal Hypoglycemic Events in Type 1 Diabetic Patients under Multiple Doses of Insulin. Sensors. 22(4). 1665–1665. 23 indexed citations

14.

Contreras, Iván, et al.. (2022). Generation of Individualized Synthetic Data for Augmentation of the Type 1 Diabetes Data Sets Using Deep Learning Models. Sensors. 22(13). 4944–4944. 16 indexed citations

15.

Contreras, Iván, Remei Calm, Miguel Saínz, Pau Herrero, & Josep Vehı́. (2021). Combining Grammatical Evolution with Modal Interval Analysis: An Application to Solve Problems with Uncertainty. Mathematics. 9(6). 631–631. 2 indexed citations

16.

Herraiz, S., et al.. (2021). Definition of Residential Power Load Profiles Clusters Using Machine Learning and Spatial Analysis. Energies. 14(20). 6565–6565. 5 indexed citations

17.

Saínz, Miguel, et al.. (2021). Marks: A New Interval Tool for Uncertainty, Vagueness and Indiscernibility. Mathematics. 9(17). 2116–2116. 1 indexed citations

18.

Vehı́, Josep, et al.. (2019). Impact of Use Frequency of a Mobile Diabetes Management App on Blood Glucose Control: Evaluation Study. JMIR mhealth and uhealth. 7(3). e11933–e11933. 17 indexed citations

19.

Contreras, Iván, et al.. (2018). Using Grammatical Evolution to Generate Short-term Blood Glucose Prediction Models.. International Joint Conference on Artificial Intelligence. 91–96. 16 indexed citations

20.

Contreras, Iván, Silvia Oviedo, Martina Vettoretti, R. Visentin, & Josep Vehı́. (2017). Personalized blood glucose prediction: A hybrid approach using grammatical evolution and physiological models. PLoS ONE. 12(11). e0187754–e0187754. 58 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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