Andrea Campagner

2.9k total citations
67 papers, 1.6k citations indexed

About

Andrea Campagner is a scholar working on Artificial Intelligence, Health Informatics and Computational Theory and Mathematics. According to data from OpenAlex, Andrea Campagner has authored 67 papers receiving a total of 1.6k indexed citations (citations by other indexed papers that have themselves been cited), including 36 papers in Artificial Intelligence, 17 papers in Health Informatics and 12 papers in Computational Theory and Mathematics. Recurrent topics in Andrea Campagner's work include Artificial Intelligence in Healthcare and Education (17 papers), Explainable Artificial Intelligence (XAI) (16 papers) and Rough Sets and Fuzzy Logic (11 papers). Andrea Campagner is often cited by papers focused on Artificial Intelligence in Healthcare and Education (17 papers), Explainable Artificial Intelligence (XAI) (16 papers) and Rough Sets and Fuzzy Logic (11 papers). Andrea Campagner collaborates with scholars based in Italy, Portugal and Spain. Andrea Campagner's co-authors include Federico Cabitza, Davide Ciucci, Giuseppe Banfi, Anna Carobene, Massimo Locatelli, Davide Ferrari, Luca Maria Sconfienza, Clara Balsano, Marília Barandas and Michela Seghezzi and has published in prestigious journals such as SHILAP Revista de lepidopterología, IEEE Transactions on Pattern Analysis and Machine Intelligence and Expert Systems with Applications.

In The Last Decade

Andrea Campagner

61 papers receiving 1.5k citations

Peers

Andrea Campagner
Qingyu Chen China
Sijia Liu United States
Kirk Roberts United States
Taxiarchis Botsis United States
Κωνσταντίνα Κούρου Greece
Guotong Xie China
Shyam Visweswaran United States
Volodymyr Kuleshov United States
Ahmed M. Alaa United States
Themis P. Exarchos Greece
Qingyu Chen China
Andrea Campagner
Citations per year, relative to Andrea Campagner Andrea Campagner (= 1×) peers Qingyu Chen

Countries citing papers authored by Andrea Campagner

Since Specialization
Citations

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

Fields of papers citing papers by Andrea Campagner

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Andrea Campagner

This figure shows the co-authorship network connecting the top 25 collaborators of Andrea Campagner. A scholar is included among the top collaborators of Andrea Campagner 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 Andrea Campagner. Andrea Campagner 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.
Salvi, Massimo, Silvia Seoni, Andrea Campagner, et al.. (2025). Explainability and uncertainty: Two sides of the same coin for enhancing the interpretability of deep learning models in healthcare. International Journal of Medical Informatics. 197. 105846–105846. 14 indexed citations
2.
Baroncini, Alice, Andrea Campagner, Federico Cabitza, et al.. (2025). The use of machine learning for the prediction of response to follow-up in spine registries. International Journal of Medical Informatics. 195. 105752–105752. 1 indexed citations
3.
Campagner, Andrea, Beatrice Arosio, Paolo Rossi, et al.. (2025). Uncovering hidden subtypes in dementia: An unsupervised machine learning approach to dementia diagnosis and personalization of care. Journal of Biomedical Informatics. 165. 104799–104799.
4.
Cabitza, Federico, et al.. (2024). Never tell me the odds: Investigating pro-hoc explanations in medical decision making. Artificial Intelligence in Medicine. 150. 102819–102819. 15 indexed citations
5.
Campagner, Andrea, et al.. (2024). Evidence-based XAI: An empirical approach to design more effective and explainable decision support systems. Computers in Biology and Medicine. 170. 108042–108042. 20 indexed citations
6.
Campagner, Andrea, et al.. (2024). Second opinion machine learning for fast-track pathway assignment in hip and knee replacement surgery: the use of patient-reported outcome measures. BMC Medical Informatics and Decision Making. 24(S4). 203–203. 3 indexed citations
7.
Campagner, Andrea. (2023). Learning from fuzzy labels: Theoretical issues and algorithmic solutions. International Journal of Approximate Reasoning. 171. 108969–108969. 3 indexed citations
8.
Campagner, Andrea, Davide Ciucci, & Thierry Denœux. (2023). A distributional framework for evaluation, comparison and uncertainty quantification in soft clustering. International Journal of Approximate Reasoning. 162. 109008–109008. 4 indexed citations
9.
Cabitza, Federico, et al.. (2023). Painting the Black Box White: Experimental Findings from Applying XAI to an ECG Reading Setting. SHILAP Revista de lepidopterología. 5(1). 269–286. 9 indexed citations
10.
Cabitza, Federico, Andrea Campagner, & Valerio Basile. (2023). Toward a Perspectivist Turn in Ground Truthing for Predictive Computing. Proceedings of the AAAI Conference on Artificial Intelligence. 37(6). 6860–6868. 21 indexed citations
11.
Barandas, Marília, et al.. (2022). Comparing Handcrafted Features and Deep Neural Representations for Domain Generalization in Human Activity Recognition. Sensors. 22(19). 7324–7324. 14 indexed citations
12.
13.
Cabitza, Federico, Andrea Campagner, & Luca Maria Sconfienza. (2021). Studying human-AI collaboration protocols: the case of the Kasparov’s law in radiological double reading. Health Information Science and Systems. 9(1). 8–8. 22 indexed citations
14.
Cabitza, Federico, Andrea Campagner, Felipe Soares, et al.. (2021). The importance of being external. methodological insights for the external validation of machine learning models in medicine. Computer Methods and Programs in Biomedicine. 208. 106288–106288. 155 indexed citations
15.
Folgado, Duarte, Sara Santos, Marília Barandas, et al.. (2021). Interpretable heartbeat classification using local model-agnostic explanations on ECGs. Computers in Biology and Medicine. 133. 104393–104393. 64 indexed citations
16.
Cabitza, Federico, Andrea Campagner, Davide Ferrari, et al.. (2020). Development, evaluation, and validation of machine learning models for COVID-19 detection based on routine blood tests. Clinical Chemistry and Laboratory Medicine (CCLM). 59(2). 421–431. 110 indexed citations
17.
Campagner, Andrea, Federico Cabitza, & Davide Ciucci. (2020). The three-way-in and three-way-out framework to treat and exploit ambiguity in data. International Journal of Approximate Reasoning. 119. 292–312. 34 indexed citations
18.
Cabitza, Federico, Andrea Campagner, & Luca Maria Sconfienza. (2020). As if sand were stone. New concepts and metrics to probe the ground on which to build trustable AI. BMC Medical Informatics and Decision Making. 20(1). 219–219. 27 indexed citations
19.
Cabitza, Federico, Andrea Campagner, & Clara Balsano. (2020). Bridging the “last mile” gap between AI implementation and operation: “data awareness” that matters. Annals of Translational Medicine. 8(7). 501–501. 65 indexed citations
20.
Campagner, Andrea, et al.. (2020). Detection of COVID-19 Infection from Routine Blood Exams with Machine Learning: A Feasibility Study. Journal of Medical Systems. 44(8). 135–135. 225 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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