Davide Chicco
Impact in
- Health Information Management top 0.1%
- Artificial Intelligence in Healthcare
- Health Informatics top 0.5%
Papers in
-
- Artificial Intelligence in Healthcare 9
- Co-authors
- Giuseppe JurmanMatthijs J. WarrensNiklas TötschMarco MasseroliPeter SadowskiPierre BaldiLuca OnetoPietro Pinoli
- Journals
- BioData Mining (12 papers)PLoS Computational Biology (11 papers)PeerJ Computer Science (7 papers)IEEE Access (5 papers)IEEE/ACM Transactions on Computational Biology and Bioinformatics (4 papers)
- Partner nations
- CanadaItalyUnited States
In The Last Decade
Davide Chicco
65 papers receiving 9.2k citations
Hit Papers
Peers
Comparison fields: 5 of 229
- Health Information Management 596
- Health Informatics 148
- Artificial Intelligence 2.5k
- Environmental Engineering 560
- Signal Processing 412
Countries citing papers authored by Davide Chicco
This map shows the geographic impact of Davide Chicco'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 Davide Chicco with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Davide Chicco more than expected).
Fields of papers citing papers by Davide Chicco
This network shows the impact of papers produced by Davide Chicco. 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 Davide Chicco. The network helps show where Davide Chicco may publish in the future.
Co-authors
The 25 scholars most cited alongside Davide Chicco, 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 | 2025 | 1 | |
| 2 | 2025 | 1 | |
| 3 | 2025 | 2 | |
| 4 | 2025 | 1 | |
| 5 | 2025 | 5 | |
| 6 | 2024 | 3 | |
| 7 | 2023 | 5 | |
| 8 | 2023 | 3 | |
| 9 | 2023 | 5 | |
| 10 | 2022 | 29 | |
| 11 | 2022 | 3 | |
| 12 | 2021 | 12 | |
| 13 | 2021 | 16 | |
| 14 | The Matthews Correlation Coefficient (MCC) is More Informative Than Cohen’s Kappa and Brier Score in Binary Classification Assessment Hit paper breakdown → | 2021 | 267 |
| 15 | 2021 | 23 | |
| 16 | 2020 | 109 | |
| 17 | 2020 | 14 | |
| 18 | The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation Hit paper breakdown → | 2020 | 3395 |
| 19 | 2020 | 38 | |
| 20 | 2017 | 1 |
About Davide Chicco
Davide Chicco is a scholar working on Health Information Management, Health Informatics, Artificial Intelligence, Information Systems and Management and Biophysics, having authored 70 papers that have together received 9.5k indexed citations. Recurring topics across this work include Gene expression and cancer classification (21 papers), Bioinformatics and Genomic Networks (16 papers), Biomedical Text Mining and Ontologies (12 papers), Artificial Intelligence in Healthcare (9 papers), Machine Learning in Healthcare (8 papers), Imbalanced Data Classification Techniques (7 papers), Genetics, Bioinformatics, and Biomedical Research (7 papers) and Scientific Computing and Data Management (5 papers). The work is most often cited by research in Health Information Management (596 citations), Health Informatics (148 citations), Artificial Intelligence (2.5k citations), Environmental Engineering (560 citations) and Signal Processing (412 citations). Davide Chicco has collaborated with scholars based in Canada, Italy and United States. Frequent co-authors include Giuseppe Jurman, Matthijs J. Warrens, Niklas Tötsch, Marco Masseroli, Peter Sadowski, Pierre Baldi, Luca Oneto, Pietro Pinoli, Cristina Rovelli and Giuseppe Agapito. Their work appears in journals such as BioData Mining, PLoS Computational Biology, PeerJ Computer Science, IEEE Access and IEEE/ACM Transactions on Computational Biology and Bioinformatics.
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.