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  1. Book ; Online: Responsible AI in Healthcare

    Cabitza, Federico / Ciucci, Davide / Pasi, Gabriella / Viviani, Marco

    2022  

    Abstract: This article discusses open problems, implemented solutions, and future research in the area of responsible AI in healthcare. In particular, we illustrate two main research themes related to the work of two laboratories within the Department of ... ...

    Abstract This article discusses open problems, implemented solutions, and future research in the area of responsible AI in healthcare. In particular, we illustrate two main research themes related to the work of two laboratories within the Department of Informatics, Systems, and Communication at the University of Milano-Bicocca. The problems addressed concern, in particular, {uncertainty in medical data and machine advice}, and the problem of online health information disorder.

    Comment: 5 pages, 0 figures
    Keywords Computer Science - Computers and Society ; Computer Science - Artificial Intelligence ; Computer Science - Information Retrieval ; Computer Science - Machine Learning
    Publishing date 2022-02-19
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

    Full text online

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  2. Book ; Online: A Distributional Approach for Soft Clustering Comparison and Evaluation

    Campagner, Andrea / Ciucci, Davide / Denœux, Thierry

    2022  

    Abstract: The development of external evaluation criteria for soft clustering (SC) has received limited attention: existing methods do not provide a general approach to extend comparison measures to SC, and are unable to account for the uncertainty represented in ... ...

    Abstract The development of external evaluation criteria for soft clustering (SC) has received limited attention: existing methods do not provide a general approach to extend comparison measures to SC, and are unable to account for the uncertainty represented in the results of SC algorithms. In this article, we propose a general method to address these limitations, grounding on a novel interpretation of SC as distributions over hard clusterings, which we call \emph{distributional measures}. We provide an in-depth study of complexity- and metric-theoretic properties of the proposed approach, and we describe approximation techniques that can make the calculations tractable. Finally, we illustrate our approach through a simple but illustrative experiment.

    Comment: This is the extended version of article "A Distributional Approach for Soft Clustering Comparison and Evaluation", accepted at BELIEF 2022 (http://hebergement.universite-paris-saclay.fr/belief2022/). Please cite the proceedings version of the article
    Keywords Computer Science - Machine Learning ; Computer Science - Artificial Intelligence
    Subject code 006
    Publishing date 2022-06-20
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  3. Article ; Online: Ordinal labels in machine learning: a user-centered approach to improve data validity in medical settings.

    Seveso, Andrea / Campagner, Andrea / Ciucci, Davide / Cabitza, Federico

    BMC medical informatics and decision making

    2020  Volume 20, Issue Suppl 5, Page(s) 142

    Abstract: Background: Despite the vagueness and uncertainty that is intrinsic in any medical act, interpretation and decision (including acts of data reporting and representation of relevant medical conditions), still little research has focused on how to ... ...

    Abstract Background: Despite the vagueness and uncertainty that is intrinsic in any medical act, interpretation and decision (including acts of data reporting and representation of relevant medical conditions), still little research has focused on how to explicitly take this uncertainty into account. In this paper, we focus on the representation of a general and wide-spread medical terminology, which is grounded on a traditional and well-established convention, to represent severity of health conditions (for instance, pain, visible signs), ranging from Absent to Extreme. Specifically, we will study how both potential patients and doctors perceive the different levels of the terminology in both quantitative and qualitative terms, and if the embedded user knowledge could improve the representation of ordinal values in the construction of machine learning models.
    Methods: To this aim, we conducted a questionnaire-based research study involving a relatively large sample of 1,152 potential patients and 31 clinicians to represent numerically the perceived meaning of standard and widely-applied labels to describe health conditions. Using these collected values, we then present and discuss different possible fuzzy-set based representations that address the vagueness of medical interpretation by taking into account the perceptions of domain experts. We also apply the findings of this user study to evaluate the impact of different encodings on the predictive performance of common machine learning models in regard to a real-world medical prognostic task.
    Results: We found significant differences in the perception of pain levels between the two user groups. We also show that the proposed encodings can improve the performances of specific classes of models, and discuss when this is the case.
    Conclusions: In perspective, our hope is that the proposed techniques for ordinal scale representation and ordinal encoding may be useful to the research community, and also that our methodology will be applied to other widely used ordinal scales for improving validity of datasets and bettering the results of machine learning tasks.
    MeSH term(s) Decision Making ; Fuzzy Logic ; Humans ; Knowledge ; Machine Learning ; Physicians ; Reproducibility of Results ; Research Design ; Surveys and Questionnaires
    Language English
    Publishing date 2020-08-20
    Publishing country England
    Document type Journal Article ; Research Support, Non-U.S. Gov't
    ISSN 1472-6947
    ISSN (online) 1472-6947
    DOI 10.1186/s12911-020-01152-8
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  4. Article: Fit of biokinetic data in molecular radiotherapy: a machine learning approach.

    Ciucci, Davide / Cassano, Bartolomeo / Donatiello, Salvatore / Martire, Federica / Napolitano, Antonio / Polito, Claudia / Solfaroli Camillocci, Elena / Cervino, Gianluca / Pungitore, Ludovica / Altini, Claudio / Villani, Maria Felicia / Pizzoferro, Milena / Garganese, Maria Carmen / Cannatà, Vittorio

    EJNMMI physics

    2024  Volume 11, Issue 1, Page(s) 19

    Abstract: Background: In literature are reported different analytical methods (AM) to choose the proper fit model and to fit data of the time-activity curve (TAC). On the other hand, Machine Learning algorithms (ML) are increasingly used for both classification ... ...

    Abstract Background: In literature are reported different analytical methods (AM) to choose the proper fit model and to fit data of the time-activity curve (TAC). On the other hand, Machine Learning algorithms (ML) are increasingly used for both classification and regression tasks. The aim of this work was to investigate the possibility of employing ML both to classify the most appropriate fit model and to predict the area under the curve (τ).
    Methods: Two different ML systems have been developed for classifying the fit model and to predict the biokinetic parameters. The two systems were trained and tested with synthetic TACs simulating a whole-body Fraction Injected Activity for patients affected by metastatic Differentiated Thyroid Carcinoma, administered with [
    Results: As N varies, CA remains constant for ML (about 98%), while it improves for F-test (from 62 to 92%) and AICc (from 50 to 92%), as N increases. With AM, [Formula: see text] can reach down to - 67%, while using ML [Formula: see text] ranges within ± 25%. Using real TACs, there is a good agreement between τ obtained with ML system and AM.
    Conclusions: The employing of ML systems may be feasible, having both a better classification and a better estimation of biokinetic parameters.
    Language English
    Publishing date 2024-02-22
    Publishing country Germany
    Document type Journal Article
    ZDB-ID 2768912-8
    ISSN 2197-7364
    ISSN 2197-7364
    DOI 10.1186/s40658-024-00623-5
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  5. Article ; Online: Three-Way Decision for Handling Uncertainty in Machine Learning: A Narrative Review

    Campagner, Andrea / Cabitza, Federico / Ciucci, Davide

    Rough Sets

    Abstract: In this work we introduce a framework, based on three-way decision (TWD) and the trisecting-acting-outcome model, to handle uncertainty in Machine Learning (ML). We distinguish between handling uncertainty affecting the input of ML models, when TWD is ... ...

    Abstract In this work we introduce a framework, based on three-way decision (TWD) and the trisecting-acting-outcome model, to handle uncertainty in Machine Learning (ML). We distinguish between handling uncertainty affecting the input of ML models, when TWD is used to identify and properly take into account the uncertain instances; and handling the uncertainty lying in the output, where TWD is used to allow the ML model to abstain. We then present a narrative review of the state of the art of applications of TWD in regard to the different areas of concern identified by the framework, and in so doing, we will highlight both the points of strength of the three-way methodology, and the opportunities for further research.
    Keywords covid19
    Publisher PMC
    Document type Article ; Online
    DOI 10.1007/978-3-030-52705-1_10
    Database COVID19

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  6. Article ; Online: Feature Reduction in Superset Learning Using Rough Sets and Evidence Theory

    Campagner, Andrea / Ciucci, Davide / Hüllermeier, Eyke

    Information Processing and Management of Uncertainty in Knowledge-Based Systems

    Abstract: Supervised learning is an important branch of machine learning (ML), which requires a complete annotation (labeling) of the involved training data. This assumption, which may constitute a severe bottleneck in the practical use of ML, is relaxed in weakly ...

    Abstract Supervised learning is an important branch of machine learning (ML), which requires a complete annotation (labeling) of the involved training data. This assumption, which may constitute a severe bottleneck in the practical use of ML, is relaxed in weakly supervised learning. In this ML paradigm, training instances are not necessarily precisely labeled. Instead, annotations are allowed to be imprecise or partial. In the setting of superset learning, instances are assumed to be labeled with a set of possible annotations, which is assumed to contain the correct one. In this article, we study the application of rough set theory in the setting of superset learning. In particular, we consider the problem of feature reduction as a mean for data disambiguation, i.e., for the purpose of figuring out the most plausible precise instantiation of the imprecise training data. To this end, we define appropriate generalizations of decision tables and reducts, using information-theoretic techniques based on evidence theory. Moreover, we analyze the complexity of the associated computational problems.
    Keywords covid19
    Publisher PMC
    Document type Article ; Online
    DOI 10.1007/978-3-030-50146-4_35
    Database COVID19

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  7. Article ; Online: Approximate Reaction Systems Based on Rough Set Theory

    Campagner, Andrea / Ciucci, Davide / Dorigatti, Valentina

    Rough Sets

    Abstract: In this work we investigate how Rough Set Theory could be employed to model uncertainty and information incompleteness about a Reaction System. The approach that we propose is inspired by the idea of an abstract scientific experiment: we define the ... ...

    Abstract In this work we investigate how Rough Set Theory could be employed to model uncertainty and information incompleteness about a Reaction System. The approach that we propose is inspired by the idea of an abstract scientific experiment: we define the notion of test, which defines an approximation space on the states of a Reaction System, and observation, to represent the interactive process of knowledge building that is typical of complex systems. We then define appropriate notions of reducts and study their characterization in terms of both computational complexity and relationships with standard definitions of reducts in terms of Information Tables.
    Keywords covid19
    Publisher PMC
    Document type Article ; Online
    DOI 10.1007/978-3-030-52705-1_4
    Database COVID19

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  8. Book ; Conference proceedings ; Online: Rough sets, fuzzy sets, data mining, and granular computing

    Ciucci, Davide / Inuiguchi, Masahiro / Ślęzak, Dominik / Wang, Guoyin / Yao, Yiyu

    14th international conference, RSFDGrC 2013, Halifax, NS, Canada, October 11 - 14, 2013 ; proceedings

    (Lecture notes in computer science : Lecture notes in artificial intelligence ; 8170)

    2013  

    Abstract: This book constitutes the thoroughly refereed conference proceedings of the 14th International Conference on Rough Sets, Fuzzy Sets, Data Mining and Granular Computing, RSFDGrC 2013, held in Halifax, Canada in October 2013 as one of the co-located ... ...

    Institution RSFDGrC
    Event/congress RSFDGrC (14, 2013.10.11-14, Halifax) ; Rough Sets, Fuzzy Sets, Data Mining and Granular Computing International Conference (14, 2013.10.11-14, Halifax)
    Author's details Davide Ciucci; Masahiro Inuiguchi; Yiyu Yao; Dominik Ślęzak; Guoyin Wang (eds.)
    Series title Lecture notes in computer science : Lecture notes in artificial intelligence ; 8170
    Abstract This book constitutes the thoroughly refereed conference proceedings of the 14th International Conference on Rough Sets, Fuzzy Sets, Data Mining and Granular Computing, RSFDGrC 2013, held in Halifax, Canada in October 2013 as one of the co-located conference of the 2013 Joint Rough Set Symposium, JRS 2013. The 69 papers (including 44 regular and 25 short papers) included in the JRS proceedings (LNCS 8170 and LNCS 8171) were carefully reviewed and selected from 106 submissions. The papers in this volume cover topics such as inconsistency, incompleteness, non-determinism; fuzzy and rough hybridization; granular computing and covering-based rough sets; soft clustering; image and medical data analysis
    Keywords Artificial intelligence ; Computer science ; Data mining ; Optical pattern recognition
    Language English
    Size Online-Ressource (XIV, 398 S.)
    Publisher Springer
    Publishing place Berlin u.a.
    Document type Book ; Conference proceedings ; Online
    Note Literaturangaben
    ISBN 3642412173 ; 9783642412172 ; 9783642412189 ; 3642412181
    DOI 10.1007/978-3-642-41218-9
    Database Former special subject collection: coastal and deep sea fishing

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  9. Book ; Conference proceedings: Rough sets, fuzzy sets, data mining, and granular computing

    Ciucci, Davide / Guoyin, Wang / Inuiguchi, Masahiro / Slezak, Dominik / Yao, Yiyu

    14th international conference, RSFDGrC 2013, Halifax, NS, Canada, October 11-14, 2013 ; proceedings

    (Lecture notes in computer science ; 8170)

    2013  

    Event/congress Joint Rough Set Symposium, JRS (2013.10.11-14, Halifax) ; Rough Sets, Fuzzy Sets, Data Mining and Granular Computing, RSFDGrC (14, 2013.10.11-14, Halifax)
    Author's details Davide Ciucci ... (ed.)
    Series title Lecture notes in computer science ; 8170
    Keywords Fuzzy-Menge ; Grobmenge ; Data Mining ; Granular Computing
    Language English
    Size XIV, 398 S., 235 mm x 155 mm
    Publisher Springer
    Publishing place Berlin u.a.
    Document type Book ; Conference proceedings
    ISBN 3642412173 ; 9783642412172 ; 9783642412189 ; 3642412181
    Database Library catalogue of the German National Library of Science and Technology (TIB), Hannover

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  10. Article: Towards a fuzzy ontology definition and a fuzzy extension of an ontology editor

    Calegari, Silvia / Ciucci, Davide

    Enterprise information systems : 8th International Conference, ICEIS 2006, Paphos, Cyprus, May 23 - 27, 2006 ; revised selected papers , p. 147-158

    2008  , Page(s) 147–158

    Author's details Silvia Calegari and Davide Ciucci
    Keywords Datenbank ; Ontologie ; Fuzzy Sets ; Datenmodell ; Informatik ; Theorie
    Language English
    Size Ill., graph. Darst.
    Publisher Springer
    Publishing place Berlin [u.a.]
    Document type Article
    ISBN 978-354-07758-0-5 ; 354-07758-0-3
    Database ECONomics Information System

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