Luis Vigil

732 total citations
33 papers, 540 citations indexed

About

Luis Vigil is a scholar working on Cardiology and Cardiovascular Medicine, Endocrinology, Diabetes and Metabolism and Nephrology. According to data from OpenAlex, Luis Vigil has authored 33 papers receiving a total of 540 indexed citations (citations by other indexed papers that have themselves been cited), including 17 papers in Cardiology and Cardiovascular Medicine, 11 papers in Endocrinology, Diabetes and Metabolism and 9 papers in Nephrology. Recurrent topics in Luis Vigil's work include Blood Pressure and Hypertension Studies (10 papers), Chronic Kidney Disease and Diabetes (8 papers) and Heart Rate Variability and Autonomic Control (7 papers). Luis Vigil is often cited by papers focused on Blood Pressure and Hypertension Studies (10 papers), Chronic Kidney Disease and Diabetes (8 papers) and Heart Rate Variability and Autonomic Control (7 papers). Luis Vigil collaborates with scholars based in Spain, United States and Australia. Luis Vigil's co-authors include Rafael García Carretero, Óscar Barquero‐Pérez, Javier Ramos, Manuel Varela, J. Segura, Luís M. Ruilope, Carlos Campo, Inmaculada Mora-Jiménez, A. John Rush and David A. Khan and has published in prestigious journals such as PLoS ONE, Biological Psychiatry and Journal of the American Society of Nephrology.

In The Last Decade

Luis Vigil

30 papers receiving 522 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Luis Vigil Spain 15 189 139 118 83 53 33 540
Jean‐Pierre Fauvel France 16 289 1.5× 111 0.8× 291 2.5× 61 0.7× 84 1.6× 68 883
E Shyong Tai Singapore 17 130 0.7× 254 1.8× 146 1.2× 87 1.0× 224 4.2× 33 1.1k
Amir Hossein Behnoush Iran 14 333 1.8× 160 1.2× 61 0.5× 126 1.5× 154 2.9× 91 778
Gabriel Coll de Tuero Spain 15 201 1.1× 259 1.9× 182 1.5× 33 0.4× 85 1.6× 62 868
Jin‐Shang Wu Taiwan 13 105 0.6× 107 0.8× 37 0.3× 81 1.0× 102 1.9× 40 528
Yasuhiro Matsubayashi Japan 15 140 0.7× 261 1.9× 18 0.2× 90 1.1× 109 2.1× 56 554
Juan José García Sánchez United Kingdom 13 343 1.8× 163 1.2× 223 1.9× 118 1.4× 32 0.6× 48 859
Miye Wang China 12 88 0.5× 107 0.8× 19 0.2× 27 0.3× 53 1.0× 26 396
Hongwei Ji China 15 544 2.9× 248 1.8× 54 0.5× 138 1.7× 127 2.4× 49 985

Countries citing papers authored by Luis Vigil

Since Specialization
Citations

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

Fields of papers citing papers by Luis Vigil

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Luis Vigil

This figure shows the co-authorship network connecting the top 25 collaborators of Luis Vigil. A scholar is included among the top collaborators of Luis Vigil 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 Luis Vigil. Luis Vigil 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.
Carretero, Rafael García, Luis Vigil, & Óscar Barquero‐Pérez. (2021). The Use of Machine Learning Techniques to Determine the Predictive Value of Inflammatory Biomarkers in the Development of Type 2 Diabetes Mellitus. Metabolic Syndrome and Related Disorders. 19(4). 240–248. 14 indexed citations
2.
Carretero, Rafael García, Luis Vigil, Óscar Barquero‐Pérez, et al.. (2021). Machine learning approaches to constructing predictive models of vitamin D deficiency in a hypertensive population: a comparative study. Informatics for Health and Social Care. 46(4). 355–369. 14 indexed citations
3.
Carretero, Rafael García, Luis Vigil, Óscar Barquero‐Pérez, et al.. (2020). Logistic LASSO and Elastic Net to Characterize Vitamin D Deficiency in a Hypertensive Obese Population. Metabolic Syndrome and Related Disorders. 18(2). 79–85. 18 indexed citations
4.
Carretero, Rafael García, Luis Vigil, Inmaculada Mora-Jiménez, et al.. (2020). Use of a K-nearest neighbors model to predict the development of type 2 diabetes within 2 years in an obese, hypertensive population. Medical & Biological Engineering & Computing. 58(5). 991–1002. 37 indexed citations
5.
6.
Carretero, Rafael García, Luis Vigil, Óscar Barquero‐Pérez, & Javier Ramos. (2019). Relevant Features in Nonalcoholic Steatohepatitis Determined Using Machine Learning for Feature Selection. Metabolic Syndrome and Related Disorders. 17(9). 444–451. 19 indexed citations
7.
Carretero, Rafael García, Luis Vigil, Inmaculada Mora-Jiménez, et al.. (2018). Cardiovascular risk assessment in prediabetic patients in a hypertensive population: The role of cystatin C. Diabetes & Metabolic Syndrome Clinical Research & Reviews. 12(5). 625–629. 7 indexed citations
8.
Cuesta–Frau, David, et al.. (2018). Classification of glucose records from patients at diabetes risk using a combined permutation entropy algorithm. Computer Methods and Programs in Biomedicine. 165. 197–204. 14 indexed citations
9.
Carretero, Rafael García, Luis Vigil, Óscar Barquero‐Pérez, et al.. (2017). Cystatin C as a predictor of cardiovascular outcomes in a hypertensive population. Journal of Human Hypertension. 31(12). 801–807. 13 indexed citations
10.
Vigil, Luis, et al.. (2014). Glucose series complexity in hypertensive patients. Journal of the American Society of Hypertension. 8(9). 630–636. 6 indexed citations
11.
Varela, Manuel, et al.. (2014). Glucose series complexity at the threshold of diabetes 糖尿病阈值的血糖序列的复杂性. Journal of Diabetes. 7(2). 287–293. 5 indexed citations
12.
Oliveras, Anna, Luis García‐Ortiz, J. Segura, et al.. (2012). Association between urinary albumin excretion and both central and peripheral blood pressure in subjects with insulin resistance. Journal of Hypertension. 31(1). 103–108. 18 indexed citations
13.
Jiménez, Manuel L., et al.. (2012). Uricemia y síndrome metabólico en pacientes con hipertensión arterial. Revista Clínica Española. 212(9). 425–431. 4 indexed citations
14.
Lundelin, Krista, et al.. (2010). Differences in complexity of glycemic profile in survivors and nonsurvivors in an intensive care unit: A pilot study*. Critical Care Medicine. 38(3). 849–854. 32 indexed citations
15.
Vigil, Luis, et al.. (2009). Cystatin C is associated with the metabolic syndrome and other cardiovascular risk factors in a hypertensive population. Journal of the American Society of Hypertension. 3(3). 201–209. 36 indexed citations
16.
17.
Vigil, Luis. (2006). El cine de ciencia ficción. 18(217). 63–68.
18.
Segura, J., et al.. (2004). Development Of Chronic Kidney Disease and Cardiovascular Prognosis in Essential Hypertensive Patients. Journal of the American Society of Nephrology. 15(6). 1616–1622. 81 indexed citations
19.
Segura, J., et al.. (2004). Hypertensive Renal Damage in Metabolic Syndrome Is Associated with Glucose Metabolism Disturbances. Journal of the American Society of Nephrology. 15(1_suppl). S37–S42. 36 indexed citations
20.
Jiménez, Manuel L., et al.. (1991). Adenosine Deaminase in the Diagnosis of Pleural Effusions. Advances in experimental medicine and biology. 309B. 195–198. 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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