Giulia Cogni
Impact in
- Health Information Management top 0.5%
- Artificial Intelligence in Healthcare
- Health Informatics top 5%
- Artificial Intelligence in Healthcare and Education
Papers in
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- Diabetes Management and Research 2
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- Bioinformatics and Genomic Networks 1
- Co-authors
- Luca Chiovato (7 shared papers)Riccardo Bellazzi (5 shared papers)Arianna Dagliati (5 shared papers)Lucia Sacchi (5 shared papers)Valentina Tibollo (4 shared papers)Pasquale De Cata (4 shared papers)Marsida Teliti (4 shared papers)Simone Marini (2 shared papers)
- Journals
- Journal of Diabetes Science and Technology (2 papers)Diabetes and Vascular Disease Research (1 paper)Journal of the American Medical Informatics Association (1 paper)Nutrition (1 paper)HORMONES (1 paper)
- Partner nations
- ItalyUnited KingdomSpain
In The Last Decade
Giulia Cogni
7 papers receiving 415 citations
Giulia Cogni's Hit Papers
Peers
Comparison fields: 5 of 71
- Health Information Management 150
- Health Informatics 22
- Endocrinology, Diabetes and Metabolism 87
- Artificial Intelligence 115
- Complementary and alternative medicine 17
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-authors
The 22 scholars most cited alongside Giulia Cogni, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | Machine Learning Methods to Predict Diabetes Complications Hit paper breakdown → | 2017 | 259 |
| 2 | 2017 | 58 | |
| 3 | 2013 | 40 | |
| 4 | 2018 | 33 | |
| 5 | 2018 | 16 | |
| 6 | 2018 | 15 | |
| 7 | Hierarchical Bayesian Logistic Regression to forecast metabolic control in type 2 DM patients. | 2016 | 7 |
About Giulia Cogni
Giulia Cogni is a scholar working on Endocrinology, Diabetes and Metabolism, Molecular Biology, Artificial Intelligence, Genetics and Surgery, having authored 7 papers that have together received 428 indexed citations. Recurring topics across this work include Diabetes Management and Research (2 papers), Artificial Intelligence in Healthcare (1 paper), Retinal Diseases and Treatments (1 paper), Genetic Syndromes and Imprinting (1 paper), Diabetes and associated disorders (1 paper), Mobile Health and mHealth Applications (1 paper), Bioinformatics and Genomic Networks (1 paper) and Liver Disease Diagnosis and Treatment (1 paper). The work is most often cited by research in Health Information Management (150 citations), Health Informatics (22 citations), Endocrinology, Diabetes and Metabolism (87 citations), Artificial Intelligence (115 citations) and Complementary and alternative medicine (17 citations). Giulia Cogni has collaborated with scholars based in Italy, United Kingdom and Spain. Frequent co-authors include Luca Chiovato, Riccardo Bellazzi, Arianna Dagliati, Lucia Sacchi, Valentina Tibollo, Pasquale De Cata, Marsida Teliti, Simone Marini, Vicente Traver and Giuseppe Fico. Their work appears in journals such as Journal of Diabetes Science and Technology, Diabetes and Vascular Disease Research, Journal of the American Medical Informatics Association, Nutrition and HORMONES.
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.