Francesca Ieva
- Artificial Intelligence top 5%
- Statistics and Probability top 2%
- Radiology, Nuclear Medicine and Imaging top 10%
- Cardiology and Cardiovascular Medicine
- Biomedical Engineering
- Co-authors
- Anna Maria PaganoniChiara MasciCarlo PiccardiLucia TajoliChristopher JacksonLinda SharplesTommaso AgasistiValeria Vitelli
- Topics
- Statistical Methods and Inference (23 papers)Radiomics and Machine Learning in Medical Imaging (15 papers)Advanced Statistical Methods and Models (13 papers)
- Journals
- SHILAP Revista de lepidopterologíaPLoS ONEScientific Reports
- Partner nations
- ItalyNetherlandsUnited Kingdom
In The Last Decade
Francesca Ieva
105 papers receiving 1.1k citations
Peers
Comparison fields: 5 of 158
- Artificial Intelligence 259
- Statistics and Probability 229
- Radiology, Nuclear Medicine and Imaging 209
- Cardiology and Cardiovascular Medicine 113
- Biomedical Engineering 108
Countries citing papers authored by Francesca Ieva
This map shows the geographic impact of Francesca Ieva'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 Francesca Ieva with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Francesca Ieva more than expected).
Fields of papers citing papers by Francesca Ieva
This network shows the impact of papers produced by Francesca Ieva. 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 Francesca Ieva. The network helps show where Francesca Ieva may publish in the future.
Co-authorship network of co-authors of Francesca Ieva
This figure shows the co-authorship network connecting the top 25 collaborators of Francesca Ieva. A scholar is included among the top collaborators of Francesca Ieva 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 Francesca Ieva. Francesca Ieva is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 1 | |
| 2 | 1 | |
| 3 | 3 | |
| 4 | 1 | |
| 5 | 8 | |
| 6 | 2 | |
| 7 | 3 | |
| 8 | 47 | |
| 9 | 3 | |
| 10 | 6 | |
| 11 | 1 | |
| 12 | 56 | |
| 13 | 3 | |
| 14 | 18 | |
| 15 | Covariance-based clustering in multivariate and functional data analysis | 5 |
| 16 | 5 | |
| 17 | 5 | |
| 18 | Applications of Bayesian Methods in Detection of Healthcare Frauds | 15 |
| 19 | 3 | |
| 20 | 7 |
About Francesca Ieva
Francesca Ieva is a scholar working on Statistics and Probability, Family Practice and Statistics, Probability and Uncertainty, having authored 114 papers that have together received 1.2k indexed citations. Recurring topics across this work include Statistical Methods and Inference (23 papers), Radiomics and Machine Learning in Medical Imaging (15 papers) and Advanced Statistical Methods and Models (13 papers). The work is most often cited by research in Statistics and Probability (229 citations), Family Practice (22 citations) and Health Information Management (43 citations). Francesca Ieva has collaborated with scholars based in Italy, Netherlands and United Kingdom. Frequent co-authors include Anna Maria Paganoni, Chiara Masci, Carlo Piccardi, Lucia Tajoli, Christopher Jackson, Linda Sharples, Tommaso Agasisti, Valeria Vitelli, Davide Pigoli and Fabrizio Ruggeri. Their work appears in journals such as SHILAP Revista de lepidopterología, PLoS ONE and Scientific Reports.
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.