Giulia Cogni
- Health Information Management top 0.5%
- Artificial Intelligence top 10%
- Endocrinology, Diabetes and Metabolism top 10%
- Radiology, Nuclear Medicine and Imaging
- Epidemiology
- Co-authors
- Luca ChiovatoArianna DagliatiRiccardo BellazziLucia SacchiValentina TibolloMarsida TelitiPasquale De CataSimone Marini
- Topics
- Diabetes Management and Research (3 papers)Diabetes, Cardiovascular Risks, and Lipoproteins (2 papers)Genetic Syndromes and Imprinting (1 paper)
- Journals
- Journal of the American Medical Informatics AssociationNutritionJournal of Diabetes Science and Technology
- Partner nations
- ItalyUnited KingdomSpain
In The Last Decade
Giulia Cogni
7 papers receiving 399 citations
Hit Papers
Peers
Comparison fields: 5 of 85
- Health Information Management 179
- Artificial Intelligence 145
- Endocrinology, Diabetes and Metabolism 134
- Radiology, Nuclear Medicine and Imaging 46
- Epidemiology 44
Countries citing papers authored by Giulia Cogni
This map shows the geographic impact of Giulia Cogni'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 Giulia Cogni with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Giulia Cogni more than expected).
Fields of papers citing papers by Giulia Cogni
This network shows the impact of papers produced by Giulia Cogni. 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 Giulia Cogni. The network helps show where Giulia Cogni may publish in the future.
Co-authorship network of co-authors of Giulia Cogni
This figure shows the co-authorship network connecting the top 25 collaborators of Giulia Cogni. A scholar is included among the top collaborators of Giulia Cogni 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 Giulia Cogni. Giulia Cogni is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 16 | |
| 2 | 30 | |
| 3 | 15 | |
| 4 | 52 | |
| 5 | Machine Learning Methods to Predict Diabetes Complicationsbreakdown → | 252 |
| 6 | Hierarchical Bayesian Logistic Regression to forecast metabolic control in type 2 DM patients. | 7 |
| 7 | 40 |
About Giulia Cogni
Giulia Cogni is a scholar working on Endocrinology, Diabetes and Metabolism, Health Information Management and Genetics, having authored 7 papers that have together received 412 indexed citations. Recurring topics across this work include Diabetes Management and Research (3 papers), Diabetes, Cardiovascular Risks, and Lipoproteins (2 papers) and Genetic Syndromes and Imprinting (1 paper). The work is most often cited by research in Health Information Management (179 citations), Health Informatics (25 citations) and Endocrinology, Diabetes and Metabolism (134 citations). Giulia Cogni has collaborated with scholars based in Italy, United Kingdom and Spain. Frequent co-authors include Luca Chiovato, Arianna Dagliati, Riccardo Bellazzi, Lucia Sacchi, Valentina Tibollo, Marsida Teliti, Pasquale De Cata, Simone Marini, Antonio Martínez-Millana and Vicente Traver. Their work appears in journals such as Journal of the American Medical Informatics Association, Nutrition and Journal of Diabetes Science and Technology.
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.