Andrea Campagner
Citata da
Citata da
Detection of COVID-19 infection from routine blood exams with machine learning: a feasibility study
D Brinati, A Campagner, D Ferrari, M Locatelli, G Banfi, F Cabitza
Journal of medical systems 44 (8), 1-12, 2020
New frontiers in explainable AI: understanding the GI to interpret the GO
F Cabitza, A Campagner, D Ciucci
International Cross-Domain Conference for Machine Learning and Knowledge …, 2019
Development, evaluation, and validation of machine learning models for COVID-19 detection based on routine blood tests
F Cabitza, A Campagner, D Ferrari, C Di Resta, D Ceriotti, E Sabetta, ...
Clinical Chemistry and Laboratory Medicine (CCLM) 1 (ahead-of-print), 2020
Exploring Medical Data Classification with Three-Way Decision Trees.
A Campagner, F Cabitza, D Ciucci
HEALTHINF, 147-158, 2019
Three-way and semi-supervised decision tree learning based on orthopartitions
A Campagner, D Ciucci
International Conference on Information Processing and Management of …, 2018
Bridging the “last mile” gap between AI implementation and operation:“data awareness” that matters
F Cabitza, A Campagner, C Balsano
Annals of translational medicine 8 (7), 2020
Orthopartitions and soft clustering: soft mutual information measures for clustering validation
A Campagner, D Ciucci
Knowledge-Based Systems 180, 51-61, 2019
Three-way decision for handling uncertainty in machine learning: a narrative review
A Campagner, F Cabitza, D Ciucci
International Joint Conference on Rough Sets, 137-152, 2020
Three–way classification: Ambiguity and abstention in machine learning
A Campagner, F Cabitza, D Ciucci
International Joint Conference on Rough Sets, 280-294, 2019
The three-way-in and three-way-out framework to treat and exploit ambiguity in data
A Campagner, F Cabitza, D Ciucci
International Journal of Approximate Reasoning 119, 292-312, 2020
Ground truthing from multi-rater labeling with three-way decision and possibility theory
A Campagner, D Ciucci, CM Svensson, MT Figge, F Cabitza
Information Sciences 545, 771-790, 2021
As if sand were stone. New concepts and metrics to probe the ground on which to build trustable AI
F Cabitza, A Campagner, LM Sconfienza
BMC Medical Informatics and Decision Making 20 (1), 1-21, 2020
Programmed inefficiencies in DSS-supported human decision making
F Cabitza, A Campagner, D Ciucci, A Seveso
International Conference on Modeling Decisions for Artificial Intelligence …, 2019
H-accuracy, an alternative metric to assess classification models in medicine
A Campagner, L Sconfienza, F Cabitza
Digital Personalized Health and Medicine; Studies in Health Technology and …, 2020
The elephant in the machine: Proposing a new metric of data reliability and its application to a medical case to assess classification reliability
F Cabitza, A Campagner, D Albano, A Aliprandi, A Bruno, V Chianca, ...
Applied Sciences 10 (11), 4014, 2020
Who wants accurate models? arguing for a different metrics to take classification models seriously
F Cabitza, A Campagner
arXiv preprint arXiv:1910.09246, 2019
Studying human-AI collaboration protocols: the case of the Kasparov’s law in radiological double reading
F Cabitza, A Campagner, LM Sconfienza
Health Information Science and Systems 9 (1), 1-20, 2021
Entropy‐based shadowed set approximation of intuitionistic fuzzy sets
A Campagner, V Dorigatti, D Ciucci
International Journal of Intelligent Systems 35 (12), 2117-2139, 2020
Collective intelligence has increased diagnostic performance compared with expert radiologists in the evaluation of knee mri
S Gitto, A Campagner, C Messina, D Albano, F Cabitza, LMM Sconfienza
Seminars in Musculoskeletal Radiology 24 (S 02), A011, 2020
Assessment and prediction of spine surgery invasiveness with machine learning techniques
A Campagner, P Berjano, C Lamartina, F Langella, G Lombardi, ...
Computers in biology and medicine 121, 103796, 2020
Il sistema al momento non può eseguire l'operazione. Riprova più tardi.
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