Publications [2021-2024]

In this section, you’ll find all the scientific publications and researches that members of the MUDI laboratory have made throughout the years.

2024 Publications
2024 The Challenges and Promises of Artifcial Intelligence in the Contemporary Society: A Critical Perspective Marconi, L., Cabitza, F. (2024). The Challenges and Promises of Artifcial Intelligence in the Contemporary Society: A Critical Perspective. URBANIANA UNIVERSITY JOURNAL, 2/2024, 83-104.2024 Three-way decision in machine learning tasks: a systematic review Campagner, A., Milella, F., Ciucci, D., Cabitza, F. (2024). Three-way decision in machine learning tasks: a systematic review. ARTIFICIAL INTELLIGENCE REVIEW, 57(9) [10.1007/s10462-024-10845-9].2024 Dissimilar Similarities: Comparing Human and Statistical Similarity Evaluation in Medical AI Cabitza, F., Famiglini, L., Campagner, A., Sconfienza, L., Fusco, S., Caccavella, V., et al. (2024). Dissimilar Similarities: Comparing Human and Statistical Similarity Evaluation in Medical AI. In Modeling Decisions for Artificial Intelligence 21st International Conference, MDAI 2024, Tokyo, Japan, August 27–31, 2024, Proceedings (pp.187-198). Springer Science and Business Media Deutschland GmbH [10.1007/978-3-031-68208-7_16].2024 Rising adoption of artificial intelligence in scientific publishing: evaluating the role, risks, and ethical implications in paper drafting and review process Carobene, A., Padoan, A., Cabitza, F., Banfi, G., Plebani, M. (2024). Rising adoption of artificial intelligence in scientific publishing: evaluating the role, risks, and ethical implications in paper drafting and review process. CLINICAL CHEMISTRY AND LABORATORY MEDICINE, 62(5), 835-843 [10.1515/cclm-2023-1136].2024 Explainable Artificial Intelligence (XAI) 2.0: A manifesto of open challenges and interdisciplinary research directions Longo, L., Brcic, M., Cabitza, F., Choi, J., Confalonieri, R., Ser, J., et al. (2024). Explainable Artificial Intelligence (XAI) 2.0: A manifesto of open challenges and interdisciplinary research directions. INFORMATION FUSION, 106(June 2024) [10.1016/j.inffus.2024.102301].2024 Second opinion machine learning for fast-track pathway assignment in hip and knee replacement surgery: the use of patient-reported outcome measures Campagner, A., Milella, F., Banfi, G., Cabitza, F. (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(4) [10.1186/s12911-024-02602-3].2024 Explanations Considered Harmful: The Impact of Misleading Explanations on Accuracy in Hybrid Human-AI Decision Making Cabitza, F., Fregosi, C., Campagner, A., Natali, C. (2024). Explanations Considered Harmful: The Impact of Misleading Explanations on Accuracy in Hybrid Human-AI Decision Making. In Explainable Artificial Intelligence Second World Conference, xAI 2024, Valletta, Malta, July 17–19, 2024, Proceedings, Part IV (pp.255-269). Springer Cham [10.1007/978-3-031-63803-9_14].2024 Unifying credal partitions and fuzzy orthopartitions Boffa, S., Ciucci, D. (2024). Unifying credal partitions and fuzzy orthopartitions. INFORMATION SCIENCES, 674(July 2024) [10.1016/j.ins.2024.120725].2024 Invisible to Machines: Designing AI that Supports Vision Work in Radiology Anichini, G., Natali, C., Cabitza, F. (2024). Invisible to Machines: Designing AI that Supports Vision Work in Radiology. COMPUTER SUPPORTED COOPERATIVE WORK [10.1007/s10606-024-09491-0].2024 Ensemble Predictors: Possibilistic Combination of Conformal Predictors for Multivariate Time Series Classification Campagner, A., Barandas, M., Folgado, D., Gamboa, H., Cabitza, F. (2024). Ensemble Predictors: Possibilistic Combination of Conformal Predictors for Multivariate Time Series Classification. IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, 1-12 [10.1109/tpami.2024.3388097].2024 Never tell me the odds: Investigating pro-hoc explanations in medical decision making Cabitza, F., Natali, C., Famiglini, L., Campagner, A., Caccavella, V., Gallazzi, E. (2024). Never tell me the odds: Investigating pro-hoc explanations in medical decision making. ARTIFICIAL INTELLIGENCE IN MEDICINE, 150(April 2024), 1-11 [10.1016/j.artmed.2024.102819].2024 Evidence-based XAI: An empirical approach to design more effective and explainable decision support systems Famiglini, L., Campagner, A., Barandas, M., La Maida, G., Gallazzi, E., Cabitza, F. (2024). Evidence-based XAI: An empirical approach to design more effective and explainable decision support systems. COMPUTERS IN BIOLOGY AND MEDICINE, 170(March 2024) [10.1016/j.compbiomed.2024.108042].2024 Partially-defined equivalence relations: Relationship with orthopartitions and connection to rough sets Boffa, S., Campagner, A., Ciucci, D. (2024). Partially-defined equivalence relations: Relationship with orthopartitions and connection to rough sets. INFORMATION SCIENCES, 657(February 2024) [10.1016/j.ins.2023.119941].2024 Three-way decisions with evaluative linguistic expressions Boffa, S., Ciucci, D. (2024). Three-way decisions with evaluative linguistic expressions. INTERNATIONAL JOURNAL OF APPROXIMATE REASONING, 164(January 2024) [10.1016/j.ijar.2023.109080].2024 Evaluation of uncertainty quantification methods in multi-label classification: A case study with automatic diagnosis of electrocardiogram Barandas, M., Famiglini, L., Campagner, A., Folgado, D., Simao, R., Cabitza, F., et al. (2024). Evaluation of uncertainty quantification methods in multi-label classification: A case study with automatic diagnosis of electrocardiogram. INFORMATION FUSION, 101(January 2024) [10.1016/j.inffus.2023.101978].
2023 Publications
2023 Editorial: Clinical Integration of Artificial Intelligence in Spine Surgery: Stepping in a new Frontier Gallazzi, E., La Maida, G., Cabitza, F. (2023). Editorial: Clinical Integration of Artificial Intelligence in Spine Surgery: Stepping in a new Frontier. FRONTIERS IN SURGERY, 10 [10.3389/fsurg.2023.1351643].2023 Where is laboratory medicine headed in the next decade? Partnership model for efficient integration and adoption of artificial intelligence into medical laboratories Carobene, A., Cabitza, F., Bernardini, S., Gopalan, R., Lennerz, J., Weir, C., et al. (2023). Where is laboratory medicine headed in the next decade? Partnership model for efficient integration and adoption of artificial intelligence into medical laboratories. CLINICAL CHEMISTRY AND LABORATORY MEDICINE, 61(4), 535-543 [10.1515/cclm-2022-1030].2023 Potentials and pitfalls of ChatGPT and natural-language artificial intelligence models for the understanding of laboratory medicine test results. An assessment by the European Federation of Clinical Chemistry and Laboratory Medicine (EFLM) Working Group on Artificial Intelligence (WG-AI) Cadamuro, J., Cabitza, F., Debeljak, Z., De Bruyne, S., Frans, G., Perez, S., et al. (2023). Potentials and pitfalls of ChatGPT and natural-language artificial intelligence models for the understanding of laboratory medicine test results. An assessment by the European Federation of Clinical Chemistry and Laboratory Medicine (EFLM) Working Group on Artificial Intelligence (WG-AI). CLINICAL CHEMISTRY AND LABORATORY MEDICINE, 61(7), 1158-1166 [10.1515/cclm-2023-0355].2023 Biomarkers for Mixed Dementia: a hard bone to bite? Preliminary analyses and promising results for a debated topic Campagner, A., Famiglini, L., Arosio, B., Rossi, P., Annoni, G., Cabitza, F. (2023). Biomarkers for Mixed Dementia: a hard bone to bite? Preliminary analyses and promising results for a debated topic. In Proceedings of the 4th Italian Workshop on Artificial Intelligence for an Ageing Society co-located with 22nd International Conference of the Italian Association for Artificial Intelligence (AIxIA 2023) (pp.136-143). CEUR-WS.2023 Explainability meets uncertainty quantification: Insights from feature-based model fusion on multimodal time series Folgado, D., Barandas, M., Famiglini, L., Santos, R., Cabitza, F., Gamboa, H. (2023). Explainability meets uncertainty quantification: Insights from feature-based model fusion on multimodal time series. INFORMATION FUSION, 100(December 2023) [10.1016/j.inffus.2023.101955].2023 The Tower of Babel in Explainable Artificial Intelligence (XAI) Schneeberger, D., Rottger, R., Cabitza, F., Campagner, A., Plass, M., Muller, H., et al. (2023). The Tower of Babel in Explainable Artificial Intelligence (XAI). In Machine Learning and Knowledge Extraction 7th IFIP TC 5, TC 12, WG 8.4, WG 8.9, WG 12.9 International Cross-Domain Conference, CD-MAKE 2023, Benevento, Italy, August 29 – September 1, 2023, Proceedings (pp.65-81). Springer Science and Business Media Deutschland GmbH [10.1007/978-3-031-40837-3_5].2023 Towards a Rigorous Calibration Assessment Framework: Advancements in Metrics, Methods, and Use Famiglini, L., Campagner, A., Cabitza, F. (2023). Towards a Rigorous Calibration Assessment Framework: Advancements in Metrics, Methods, and Use. In ECAI 2023. 26th European Conference on Artificial Intelligence. September 30–October 4, 2023, Kraków, Poland. Including 12th Conference on Prestigious Applications of Intelligent Systems (PAIS 2023). Proceedings (pp.645-652). IOS Press BV [10.3233/FAIA230327].2023 Demo: Decision Support System Quality Assessment Tool Cabitza, F., Campagner, A., Natali, C. (2023). Demo: Decision Support System Quality Assessment Tool. In CHItaly '23: Proceedings of the 15th Biannual Conference of the Italian SIGCHI Chapter. Association for Computing Machinery [10.1145/3605390.3610825].2023 Aggregation Operators on Shadowed Sets Deriving from Conditional Events and Consensus Operators Boffa, S., Campagner, A., Ciucci, D., Yao, Y. (2023). Aggregation Operators on Shadowed Sets Deriving from Conditional Events and Consensus Operators. In Rough Sets International Joint Conference, IJCRS 2023, Krakow, Poland, October 5–8, 2023, Proceedings (pp.201-215). Springer Science and Business Media Deutschland GmbH [10.1007/978-3-031-50959-9_14].2023 Color Shadows 2: Assessing the Impact of XAI on Diagnostic Decision-Making Natali, C., Famiglini, L., Campagner, A., La Maida, G., Gallazzi, E., Cabitza, F. (2023). Color Shadows 2: Assessing the Impact of XAI on Diagnostic Decision-Making. In Explainable Artificial Intelligence First World Conference, xAI 2023, Lisbon, Portugal, July 26–28, 2023, Proceedings, Part I (pp.618-629). Springer Science and Business Media Deutschland GmbH [10.1007/978-3-031-44064-9_33].2023 Let Me Think! Investigating the Effect of Explanations Feeding Doubts About the AI Advice Cabitza, F., Campagner, A., Famiglini, L., Natali, C., Caccavella, V., Gallazzi, E. (2023). Let Me Think! Investigating the Effect of Explanations Feeding Doubts About the AI Advice. In Machine Learning and Knowledge Extraction 7th IFIP TC 5, TC 12, WG 8.4, WG 8.9, WG 12.9 International Cross-Domain Conference, CD-MAKE 2023, Benevento, Italy, August 29 – September 1, 2023, Proceedings (pp.155-169). Springer Science and Business Media Deutschland GmbH [10.1007/978-3-031-40837-3_10].2023 Controllable AI - An Alternative to Trustworthiness in Complex AI Systems? Kieseberg, P., Weippl, E., Tjoa, A., Cabitza, F., Campagner, A., Holzinger, A. (2023). Controllable AI - An Alternative to Trustworthiness in Complex AI Systems?. In Machine Learning and Knowledge Extraction 7th IFIP TC 5, TC 12, WG 8.4, WG 8.9, WG 12.9 International Cross-Domain Conference, CD-MAKE 2023, Benevento, Italy, August 29 – September 1, 2023, Proceedings (pp.1-12). Springer Science and Business Media Deutschland GmbH [10.1007/978-3-031-40837-3_1].2023 Enhancing human-AI collaboration: The case of colonoscopy Introzzi, L., Zonca, J., Cabitza, F., Cherubini, P., Reverberi, C. (2023). Enhancing human-AI collaboration: The case of colonoscopy. DIGESTIVE AND LIVER DISEASE [10.1016/j.dld.2023.10.018].2023 The Impact of Gender and Personality in Human-AI Teaming: The Case of Collaborative Question Answering Milella, F., Natali, C., Scantamburlo, T., Campagner, A., Cabitza, F. (2023). The Impact of Gender and Personality in Human-AI Teaming: The Case of Collaborative Question Answering. In Human-Computer Interaction – INTERACT 2023 19th IFIP TC13 International Conference, York, UK, August 28 – September 1, 2023, Proceedings, Part II (pp.329-349). Springer Cham [10.1007/978-3-031-42283-6_19].2023 Toward a Perspectivist Turn in Ground Truthing for Predictive Computing Cabitza, F., Campagner, A., Basile, V. (2023). Toward a Perspectivist Turn in Ground Truthing for Predictive Computing. In Proceedings of the 37th AAAI Conference on Artificial Intelligence, AAAI 2023 (pp.6860-6868). Washington, DC : AAAI Press [10.1609/aaai.v37i6.25840].2023 A distributional framework for evaluation, comparison and uncertainty quantification in soft clustering Campagner, A., Ciucci, D., Denoeux, T. (2023). A distributional framework for evaluation, comparison and uncertainty quantification in soft clustering. INTERNATIONAL JOURNAL OF APPROXIMATE REASONING, 162(November 2023) [10.1016/j.ijar.2023.109008].2023 Artificial Intelligence and liver: Opportunities and barriers Balsano, C., Burra, P., Duvoux, C., Alisi, A., Piscaglia, F., Gerussi, A., et al. (2023). Artificial Intelligence and liver: Opportunities and barriers. DIGESTIVE AND LIVER DISEASE, 55(11 (November 2023)), 1455-1461 [10.1016/j.dld.2023.08.048].2023 The HIBAD Experience: Using Digital Health Technologies in the GDPR Era Ferri, A., Agrati, S., Cabitza, F., Colombo, R., Filetti, S., Galeone, C., et al. (2023). The HIBAD Experience: Using Digital Health Technologies in the GDPR Era. HEALTH POLICY AND TECHNOLOGY, 12(4 (December 2023)) [10.1016/j.hlpt.2023.100788].2023 Everything is varied: The surprising impact of instantial variation on ML reliability Campagner, A., Famiglini, L., Carobene, A., Cabitza, F. (2023). Everything is varied: The surprising impact of instantial variation on ML reliability. APPLIED SOFT COMPUTING, 146(October 2023) [10.1016/j.asoc.2023.110644].2023 The Effect of Holographic Heart Models and Mixed Reality for Anatomy Learning in Congenital Heart Disease: An Exploratory Study D'Aiello, A., Cabitza, F., Natali, C., Vigano, S., Ferrero, P., Bognoni, L., et al. (2023). The Effect of Holographic Heart Models and Mixed Reality for Anatomy Learning in Congenital Heart Disease: An Exploratory Study. JOURNAL OF MEDICAL SYSTEMS, 47(1) [10.1007/s10916-023-01959-8].2023 Painting the Black Box White: Experimental Findings from Applying XAI to an ECG Reading Setting Cabitza, F., Campagner, A., Natali, C., Parimbelli, E., Ronzio, L., Cameli, M. (2023). Painting the Black Box White: Experimental Findings from Applying XAI to an ECG Reading Setting. MACHINE LEARNING AND KNOWLEDGE EXTRACTION, 5(1), 269-286 [10.3390/make5010017].2023 Assessment of Fast-Track Pathway in Hip and Knee Replacement Surgery by Propensity Score Matching on Patient-Reported Outcomes Campagner, A., Milella, F., Guida, S., Bernareggi, S., Banfi, G., Cabitza, F. (2023). Assessment of Fast-Track Pathway in Hip and Knee Replacement Surgery by Propensity Score Matching on Patient-Reported Outcomes. DIAGNOSTICS, 13(6) [10.3390/diagnostics13061189].2023 Rams, hounds and white boxes: Investigating human–AI collaboration protocols in medical diagnosis Cabitza, F., Campagner, A., Ronzio, L., Cameli, M., Mandoli, G., Pastore, M., et al. (2023). Rams, hounds and white boxes: Investigating human–AI collaboration protocols in medical diagnosis. ARTIFICIAL INTELLIGENCE IN MEDICINE, 138(April 2023) [10.1016/j.artmed.2023.102506].2023 AI Shall Have No Dominion: on How to Measure Technology Dominance in AI-supported Human decision-making Cabitza, F., Campagner, A., Angius, R., Natali, C., Reverberi, F. (2023). AI Shall Have No Dominion: on How to Measure Technology Dominance in AI-supported Human decision-making. In CHI '23: Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (pp.1-20). Association for Computing Machinery, New York, NY, United States [10.1145/3544548.3581095].2023 Logical entropy and aggregation of fuzzy orthopartitions Boffa, S., Ciucci, D. (2023). Logical entropy and aggregation of fuzzy orthopartitions. FUZZY SETS AND SYSTEMS, 455(15 March 2023), 77-101 [10.1016/j.fss.2022.07.014].2023 Orthopartitions and possibility distributions Boffa, S., Ciucci, D. (2023). Orthopartitions and possibility distributions. FUZZY SETS AND SYSTEMS, 455(15 March 2023), 53-76 [10.1016/j.fss.2022.04.022].2023 A general framework for evaluating and comparing soft clusterings Campagner, A., Ciucci, D., Denoeux, T. (2023). A general framework for evaluating and comparing soft clusterings. INFORMATION SCIENCES, 623(April 2023), 70-93 [10.1016/j.ins.2022.11.114].2023 Extracting concepts from fuzzy relational context families Boffa, S. (2023). Extracting concepts from fuzzy relational context families. IEEE TRANSACTIONS ON FUZZY SYSTEMS, 31(4 (April 2023)), 1202-1213 [10.1109/TFUZZ.2022.3197826].2023 Aggregation models in ensemble learning: A large-scale comparison Campagner, A., Ciucci, D., Cabitza, F. (2023). Aggregation models in ensemble learning: A large-scale comparison. INFORMATION FUSION, 90(February 2023), 241-252 [10.1016/j.inffus.2022.09.015].2023 Quod erat demonstrandum? - Towards a typology of the concept of explanation for the design of explainable AI Cabitza, F., Campagner, A., Malgieri, G., Natali, C., Schneeberger, D., Stoeger, K., et al. (2023). Quod erat demonstrandum? - Towards a typology of the concept of explanation for the design of explainable AI. EXPERT SYSTEMS WITH APPLICATIONS, 213(1 March 2023) [10.1016/j.eswa.2022.118888].
2022 Publications
2022 A parsimonious machine learning approach to detect inappropriate treatments in spine surgery on the basis of patient-reported outcomes Famiglini, L., Milella, F., Berjano, P., Cabitza, F. (2022). A parsimonious machine learning approach to detect inappropriate treatments in spine surgery on the basis of patient-reported outcomes. In IADIS International Conference ICT, Society and Human Beings 2022 (part of MCCSIS 2022) (pp.220-227). IADIS Press.2022 Open, Multiple, Adjunct. Decision Support at the Time of Relational AI Cabitza, F., Natali, C. (2022). Open, Multiple, Adjunct. Decision Support at the Time of Relational AI. In Proceedings of the First International Conference on Hybrid Human-Artificial Intelligence (pp.243-245). IOS press [10.3233/FAIA220204].2022 A Correspondence Between Credal Partitions and Fuzzy Orthopartitions Boffa, S., Ciucci, D. (2022). A Correspondence Between Credal Partitions and Fuzzy Orthopartitions. In Belief Functions: Theory and Applications 7th International Conference, BELIEF 2022, Paris, France, October 26–28, 2022, Proceedings (pp.251-260). Springer Science and Business Media Deutschland GmbH [10.1007/978-3-031-17801-6_24].2022 Scikit-Weak: A Python Library for Weakly Supervised Machine Learning Campagner, A., Lienen, J., Hullermeier, E., Ciucci, D. (2022). Scikit-Weak: A Python Library for Weakly Supervised Machine Learning. In Rough Sets : International Joint Conference, IJCRS 2022, Suzhou, China, November 11–14, 2022, Proceedings (pp.57-70). Springer Science and Business Media Deutschland GmbH [10.1007/978-3-031-21244-4_5].2022 Orthopartitions in Knowledge Representation and Machine Learning Ciucci, D., Boffa, S., Campagner, A. (2022). Orthopartitions in Knowledge Representation and Machine Learning. In Rough Sets - International Joint Conference, IJCRS 2022, Suzhou, China, November 11–14, 2022, Proceedings (pp.3-18). Springer Science and Business Media Deutschland GmbH [10.1007/978-3-031-21244-4_1].2022 Comparing Handcrafted Features and Deep Neural Representations for Domain Generalization in Human Activity Recognition Bento, N., Rebelo, J., Barandas, M., Carreiro, A., Campagner, A., Cabitza, F., et al. (2022). Comparing Handcrafted Features and Deep Neural Representations for Domain Generalization in Human Activity Recognition. SENSORS, 22(19) [10.3390/s22197324].2022 Three-way Learnability: A Learning Theoretic Perspective on Three-way Decision Campagner, A., Ciucci, D. (2022). Three-way Learnability: A Learning Theoretic Perspective on Three-way Decision. In Proceedings of the 17th Conference on Computer Science and Intelligence Systems, FedCSIS 2022 (pp.243-246). Polish Information Processing Society [10.15439/2022F18].2022 Application of Machine Learning to Improve Appropriateness of Treatment in an Orthopaedic Setting of Personalized Medicine Milella, F., Famiglini, L., Banfi, G., Cabitza, F. (2022). Application of Machine Learning to Improve Appropriateness of Treatment in an Orthopaedic Setting of Personalized Medicine. JOURNAL OF PERSONALIZED MEDICINE, 12(10) [10.3390/jpm12101706].2022 Detecting the Effect Size of Weather Conditions on Patient-Reported Outcome Measures (PROMs) Milella, F., Seveso, A., Famiglini, L., Banfi, G., Cabitza, F. (2022). Detecting the Effect Size of Weather Conditions on Patient-Reported Outcome Measures (PROMs). JOURNAL OF PERSONALIZED MEDICINE, 12(11) [10.3390/jpm12111811].2022 How is test laboratory data used and characterised by machine learning models? A systematic review of diagnostic and prognostic models developed for COVID-19 patients using only laboratory data Carobene, A., Milella, F., Famiglini, L., Cabitza, F. (2022). How is test laboratory data used and characterised by machine learning models? A systematic review of diagnostic and prognostic models developed for COVID-19 patients using only laboratory data. CLINICAL CHEMISTRY AND LABORATORY MEDICINE, 60(12), 1887-1901 [10.1515/cclm-2022-0182].2022 Graded cubes of opposition in fuzzy formal concept analysis Boffa, S., Murinova, P., Novak, V., Ferbas, P. (2022). Graded cubes of opposition in fuzzy formal concept analysis. INTERNATIONAL JOURNAL OF APPROXIMATE REASONING, 145, 187-209 [10.1016/j.ijar.2022.03.006].2022 A Distributional Approach for Soft Clustering Comparison and Evaluation Campagner, A., Ciucci, D., Denœux, T. (2022). A Distributional Approach for Soft Clustering Comparison and Evaluation. In 7th International Conference, BELIEF 2022, Paris, France, October 26–28, 2022, Proceedings (pp.3-12). Springer Science and Business Media Deutschland GmbH [10.1007/978-3-031-17801-6_1].2022 Granular Computing and Three-way Decisions for Cognitive Analytics Yao, J., Yao, Y., Ciucci, D., Huang, K. (2022). Granular Computing and Three-way Decisions for Cognitive Analytics. COGNITIVE COMPUTATION, 14(6), 1801-1804 [10.1007/s12559-022-10028-0].2022 Rough-set Based Genetic Algorithms for Weakly Supervised Feature Selection Campagner, A., Ciucci, D. (2022). Rough-set Based Genetic Algorithms for Weakly Supervised Feature Selection. In Communications in Computer and Information Science (pp.761-773). Springer Science and Business Media Deutschland GmbH [10.1007/978-3-031-08974-9_60].2022 Global Interpretable Calibration Index, a New Metric to Estimate Machine Learning Models’ Calibration Cabitza, F., Campagner, A., Famiglini, L. (2022). Global Interpretable Calibration Index, a New Metric to Estimate Machine Learning Models’ Calibration. In 6th IFIP TC 5, TC 12, WG 8.4, WG 8.9, WG 12.9 International Cross-Domain Conference for Machine Learning and Knowledge Extraction, CD-MAKE 2022, held in conjunction with the 17th International Conference on Availability, Reliability and Security, ARES 2022 (pp.82-99). Springer Science and Business Media Deutschland GmbH [10.1007/978-3-031-14463-9_6].2022 Color Shadows (Part I): Exploratory Usability Evaluation of Activation Maps in Radiological Machine Learning Cabitza, F., Campagner, A., Famiglini, L., Gallazzi, E., La Maida, G. (2022). Color Shadows (Part I): Exploratory Usability Evaluation of Activation Maps in Radiological Machine Learning. In Machine Learning and Knowledge Extraction - 6th IFIP TC 5, TC 12, WG 8.4, WG 8.9, WG 12.9 International Cross-Domain Conference, CD-MAKE 2022, Vienna, Austria, August 23–26, 2022, Proceedings (pp.31-50). Springer Science and Business Media Deutschland GmbH [10.1007/978-3-031-14463-9_3].2022 Re-calibrating Machine Learning Models Using Confidence Interval Bounds Campagner, A., Famiglini, L., Cabitza, F. (2022). Re-calibrating Machine Learning Models Using Confidence Interval Bounds. In 19th International Conference on Modeling Decisions for Artificial Intelligence, MDAI 2022 (pp.132-142). Springer Science and Business Media Deutschland GmbH [10.1007/978-3-031-13448-7_11].2022 A robust and parsimonious machine learning method to predict ICU admission of COVID-19 patients Famiglini, L., Campagner, A., Carobene, A., Cabitza, F. (2022). A robust and parsimonious machine learning method to predict ICU admission of COVID-19 patients. MEDICAL & BIOLOGICAL ENGINEERING & COMPUTING [10.1007/s11517-022-02543-x].2022 The unbearable (technical) unreliability of automated facial emotion recognition Cabitza, F., Campagner, A., Mattioli, M. (2022). The unbearable (technical) unreliability of automated facial emotion recognition. BIG DATA & SOCIETY, 9(2) [10.1177/20539517221129549].2022 Decisions are not all equal—Introducing a utility metric based on case-wise raters’ perceptions Campagner, A., Sternini, F., Cabitza, F. (2022). Decisions are not all equal—Introducing a utility metric based on case-wise raters’ perceptions. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE, 221(June 2022) [10.1016/j.cmpb.2022.106930].2022 A Confidence Interval-Based Method for Classifier Re-Calibration Campagner, A., Famiglini, L., Cabitza, F. (2022). A Confidence Interval-Based Method for Classifier Re-Calibration. In Studies in Health Technology and Informatics (pp.127-128). IOS Press [10.3233/SHTI220413].2022 Information Processing and Management of Uncertainty in Knowledge-Based Systems 19th International Conference, IPMU 2022, Milan, Italy, July 11–15, 2022, Proceedings, Part II Ciucci, D., Couso, I., Medina, J., Ślęzak, D., Petturiti, D., Bouchon-Meunier, B., et al. (a cura di). (2022). Information Processing and Management of Uncertainty in Knowledge-Based Systems 19th International Conference, IPMU 2022, Milan, Italy, July 11–15, 2022, Proceedings, Part II. Cham : Springer [10.1007/978-3-031-08974-9].2022 Information Processing and Management of Uncertainty in Knowledge-Based Systems - 19th International Conference, IPMU 2022, Milan, Italy, July 11–15, 2022, Proceedings, Part I Ciucci, D., Couso, I., Medina, J., Ślęzak, D., Petturiti, D., Bouchon-Meunier, B., et al. (a cura di). (2022). Information Processing and Management of Uncertainty in Knowledge-Based Systems - 19th International Conference, IPMU 2022, Milan, Italy, July 11–15, 2022, Proceedings, Part I. Cham : Springer [10.1007/978-3-031-08971-8].2022 Comparing Hexagons of Opposition in Probabilistic Rough Set Theory Boffa, S., Ciucci, D., Murinova, P. (2022). Comparing Hexagons of Opposition in Probabilistic Rough Set Theory. In Information Processing and Management of Uncertainty in Knowledge-Based Systems 19th International Conference, IPMU 2022, Milan, Italy, July 11–15, 2022, Proceedings, Part I (pp.622-633) [10.1007/978-3-031-08971-8_51].2022 Aggregation operators on shadowed sets Boffa, S., Campagner, A., Ciucci, D., Yao, Y. (2022). Aggregation operators on shadowed sets. INFORMATION SCIENCES, 595(May 2022), 313-333 [10.1016/j.ins.2022.02.046].2022 Belief functions and rough sets: Survey and new insights Campagner, A., Ciucci, D., Denoeux, T. (2022). Belief functions and rough sets: Survey and new insights. INTERNATIONAL JOURNAL OF APPROXIMATE REASONING, 143(April 2022), 192-215 [10.1016/j.ijar.2022.01.011].2022 Uncertainty representation in dynamical systems using rough set theory Campagner, A., Ciucci, D., Dorigatti, V. (2022). Uncertainty representation in dynamical systems using rough set theory. THEORETICAL COMPUTER SCIENCE, 908(24 March 2022), 28-42 [10.1016/j.tcs.2021.11.009].2022 The multicenter European Biological Variation Study (EuBIVAS): A new glance provided by the Principal Component Analysis (PCA), a machine learning unsupervised algorithms, based on the basic metabolic panel linked measurands Carobene, A., Campagner, A., Uccheddu, C., Banfi, G., Vidali, M., Cabitza, F. (2022). The multicenter European Biological Variation Study (EuBIVAS): A new glance provided by the Principal Component Analysis (PCA), a machine learning unsupervised algorithms, based on the basic metabolic panel linked measurands. CLINICAL CHEMISTRY AND LABORATORY MEDICINE, 60(4), 556-568 [10.1515/cclm-2021-0599].
2021 Publications
2021 Governare l'intelligenza artificiale. Introduzione Cabitza, F., Rossetti, A., Pozzolo, S. (2021). Governare l'intelligenza artificiale. Introduzione. RAGION PRATICA(2), 325-326 [10.1415/102316].2021 Machine Learning for Health: Algorithm Auditing & Quality Control Oala, L., Murchison, A., Balachandran, P., Choudhary, S., Fehr, J., Leite, A., et al. (2021). Machine Learning for Health: Algorithm Auditing & Quality Control. JOURNAL OF MEDICAL SYSTEMS, 45(12) [10.1007/s10916-021-01783-y].2021 A proposal to extend Relational Concept Analysis with fuzzy scaling quantifiers [Formula presented] Boffa, S., Murinova, P., Novak, V. (2021). A proposal to extend Relational Concept Analysis with fuzzy scaling quantifiers [Formula presented]. KNOWLEDGE-BASED SYSTEMS, 231 [10.1016/j.knosys.2021.107452].2021 Graded polygons of opposition in fuzzy formal concept analysis Boffa, S., Murinova, P., Novak, V. (2021). Graded polygons of opposition in fuzzy formal concept analysis. INTERNATIONAL JOURNAL OF APPROXIMATE REASONING, 132, 128-153 [10.1016/j.ijar.2021.02.007].2021 Machine Learning based on laboratory medicine test results in diagnosis and prognosis for COVID-19 patients: A systematic review Carobene, A., Sabetta, E., Monteverde, E., Locatelli, M., Banfi, G., Di Resta, C., et al. (2021). Machine Learning based on laboratory medicine test results in diagnosis and prognosis for COVID-19 patients: A systematic review. BIOCHIMICA CLINICA, 45(4), 348-364 [10.19186/BC_2021.046].2021 External validation of Machine Learning models for COVID-19 detection based on Complete Blood Count Campagner, A., Carobene, A., Cabitza, F. (2021). External validation of Machine Learning models for COVID-19 detection based on Complete Blood Count. HEALTH INFORMATION SCIENCE AND SYSTEMS, 9(1) [10.1007/s13755-021-00167-3].2021 Fuzzy orthopartitions and their logical entropy Boffa, S., Ciucci, D. (2021). Fuzzy orthopartitions and their logical entropy. In Proceedings of WILF 2021, the 13th International Workshop on Fuzzy Logic and Applications (WILF 2021), Vietri sul Mare, Italy, December 20–22, 2021 (pp.1-7). CEUR-WS.2021 Assessing the impact of medical AI: A survey of physicians' perceptions Cabitza, F., Campagner, A., Cavosi, V. (2021). Assessing the impact of medical AI: A survey of physicians' perceptions. In ACM International Conference Proceeding Series (pp.225-231). Association for Computing Machinery [10.1145/3472813.3473195].2021 To Err is (only) Human. Reflections on How to Move from Accuracy to Trust for Medical AI Cabitza, F., Campagner, A., Datteri, E. (2021). To Err is (only) Human. Reflections on How to Move from Accuracy to Trust for Medical AI. In Exploring Innovation in a Digital World: Cultural and Organizational Challenges (pp.36-49). Cham : Springer Science and Business Media Deutschland GmbH [10.1007/978-3-030-87842-9_4].2021 Weighted Utility: A Utility Metric Based on the Case-Wise Raters’ Perceptions Campagner, A., Conte, E., Cabitza, F. (2021). Weighted Utility: A Utility Metric Based on the Case-Wise Raters’ Perceptions. In Machine Learning and Knowledge Extraction - 5th IFIP TC 5, TC 12, WG 8.4, WG 8.9, WG 12.9 International Cross-Domain Conference, CD-MAKE 2021, Virtual Event, August 17–20, 2021, Proceedings (pp.203-210). Cham : Springer Science and Business Media Deutschland GmbH [10.1007/978-3-030-84060-0_13].2021 Rough Sets Ramanna, S., Cornelis, C., Ciucci, D. (a cura di). (2021). Rough Sets. Springer [10.1007/978-3-030-87334-9].2021 Feature Selection and Disambiguation in Learning from Fuzzy Labels Using Rough Sets Campagner, A., Ciucci, D. (2021). Feature Selection and Disambiguation in Learning from Fuzzy Labels Using Rough Sets. In IJCRS: International Joint Conference on Rough Sets (pp.164-179). Springer Science and Business Media Deutschland GmbH [10.1007/978-3-030-87334-9_14].2021 Possibility Distributions Generated by Intuitionistic L -Fuzzy Sets Boffa, S., Ciucci, D. (2021). Possibility Distributions Generated by Intuitionistic L -Fuzzy Sets. In Rough Sets. International Joint Conference, IJCRS 2021, Bratislava, Slovakia, September 19–24, 2021, Proceedings (pp.149-163) [10.1007/978-3-030-87334-9_13].2021 Decision-theoretic five-way approximation of fuzzy sets William-West, T., Ciucci, D. (2021). Decision-theoretic five-way approximation of fuzzy sets. INFORMATION SCIENCES, 572(September 2021), 200-222 [10.1016/j.ins.2021.04.105].2021 Identification of SARS-CoV-2 positivity using machine learning methods on blood count data: External validation of state-of-the-art models. [Identificazione di positività al SARS-CoV-2 attraverso metodi di Machine Learning sui dati dell'esame emocromocitometrico: Validazione esterna di modelli allo stato dell'arte] Carobene, A., Campagner, A., Sulejmani, A., Leoni, V., Seghezzi, M., Buoro, S., et al. (2021). Identification of SARS-CoV-2 positivity using machine learning methods on blood count data: External validation of state-of-the-art models. [Identificazione di positività al SARS-CoV-2 attraverso metodi di Machine Learning sui dati dell'esame emocromocitometrico: Validazione esterna di modelli allo stato dell'arte]. BIOCHIMICA CLINICA, 45(3), 281-289 [10.19186/BC_2021.033].2021 Has the flood entered the basement? A systematic literature review about machine learning in laboratory medicine Ronzio, L., Cabitza, F., Barbaro, A., Banfi, G. (2021). Has the flood entered the basement? A systematic literature review about machine learning in laboratory medicine. DIAGNOSTICS, 11(2) [10.3390/diagnostics11020372].2021 Three-way decision and conformal prediction: Isomorphisms, differences and theoretical properties of cautious learning approaches Campagner, A., Cabitza, F., Berjano, P., Ciucci, D. (2021). Three-way decision and conformal prediction: Isomorphisms, differences and theoretical properties of cautious learning approaches. INFORMATION SCIENCES, 579(November 2021), 347-367 [10.1016/j.ins.2021.08.009].2021 Prediction of ICU admission for COVID-19 patients: A machine learning approach based on complete blood count data Famiglini, L., Bini, G., Carobene, A., Campagner, A., Cabitza, F. (2021). Prediction of ICU admission for COVID-19 patients: A machine learning approach based on complete blood count data. In 34th IEEE International Symposium on Computer-Based Medical Systems, CBMS 2021 (pp.160-165). Institute of Electrical and Electronics Engineers Inc. [10.1109/CBMS52027.2021.00065].2021 The need to move away from agential-AI: Empirical investigations, useful concepts and open issues Cabitza, F., Campagner, A., Simone, C. (2021). The need to move away from agential-AI: Empirical investigations, useful concepts and open issues. INTERNATIONAL JOURNAL OF HUMAN-COMPUTER STUDIES, 155(November 2021) [10.1016/j.ijhcs.2021.102696].2021 The importance of being external. methodological insights for the external validation of machine learning models in medicine Cabitza, F., Campagner, A., Soares, F., Garcia de Guadiana-Romualdo, L., Challa, F., Sulejmani, A., 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(September 2021) [10.1016/j.cmpb.2021.106288].2021 Unity is intelligence: a collective intelligence experiment on ecg reading to improve diagnostic performance in cardiology Ronzio, L., Campagner, A., Cabitza, F., Gensini, G. (2021). Unity is intelligence: a collective intelligence experiment on ecg reading to improve diagnostic performance in cardiology. JOURNAL OF INTELLIGENCE, 9(2) [10.3390/jintelligence9020017].2021 The need to separate the wheat from the chaff in medical informatics: Introducing a comprehensive checklist for the (self)-assessment of medical AI studies Cabitza, F., Campagner, A. (2021). The need to separate the wheat from the chaff in medical informatics: Introducing a comprehensive checklist for the (self)-assessment of medical AI studies. INTERNATIONAL JOURNAL OF MEDICAL INFORMATICS, 153(September 2021) [10.1016/j.ijmedinf.2021.104510].2021 Rough set-based feature selection for weakly labeled data Campagner, A., Ciucci, D., Hullermeier, E. (2021). Rough set-based feature selection for weakly labeled data. INTERNATIONAL JOURNAL OF APPROXIMATE REASONING, 136(September 2021), 150-167 [10.1016/j.ijar.2021.06.005].2021 Interpretable heartbeat classification using local model-agnostic explanations on ECGs Neves, I., Folgado, D., Santos, S., Barandas, M., Campagner, A., Ronzio, L., et al. (2021). Interpretable heartbeat classification using local model-agnostic explanations on ECGs. COMPUTERS IN BIOLOGY AND MEDICINE, 133(June 2021) [10.1016/j.compbiomed.2021.104393].2021 Applications of deep learning in dentistry Corbella, S., Srinivas, S., Cabitza, F. (2021). Applications of deep learning in dentistry. ORAL SURGERY, ORAL MEDICINE, ORAL PATHOLOGY AND ORAL RADIOLOGY, 132(2 (August 2021)), 225-238 [10.1016/j.oooo.2020.11.003].2021 Studying human-AI collaboration protocols: the case of the Kasparov's law in radiological double reading Cabitza, F., Campagner, A., Sconfienza, L. (2021). Studying human-AI collaboration protocols: the case of the Kasparov's law in radiological double reading. HEALTH INFORMATION SCIENCE AND SYSTEMS, 9(1) [10.1007/s13755-021-00138-8].2021 Development, evaluation, and validation of machine learning models for COVID-19 detection based on routine blood tests Cabitza, F., Campagner, A., Ferrari, D., Di Resta, C., Ceriotti, D., Sabetta, E., et al. (2021). Development, evaluation, and validation of machine learning models for COVID-19 detection based on routine blood tests. CLINICAL CHEMISTRY AND LABORATORY MEDICINE, 59(2), 421-431 [10.1515/cclm-2020-1294].2021 Ground truthing from multi-rater labeling with three-way decision and possibility theory Campagner, A., Ciucci, D., Svensson, C., Figge, M., Cabitza, F. (2021). Ground truthing from multi-rater labeling with three-way decision and possibility theory. INFORMATION SCIENCES, 545, 771-790 [10.1016/j.ins.2020.09.049].