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212 results on '"Moret-Bonillo, Vicente"'

1. Evaluating Explanatory Capabilities of Machine Learning Models in Medical Diagnostics: A Human-in-the-Loop Approach

Author

Bobes-Bascarán, José, Mosqueira-Rey, Eduardo, Fernández-Leal, Ángel, Hernández-Pereira, Elena, Alonso-Ríos, David, Moret-Bonillo, Vicente, Figueirido-Arnoso, Israel, and Vidal-Ínsua, Yolanda

Subjects
Computer Science - Machine Learning, Computer Science - Artificial Intelligence, I.2
Abstract

This paper presents a comprehensive study on the evaluation of explanatory capabilities of machine learning models, with a focus on Decision Trees, Random Forest and XGBoost models using a pancreatic cancer dataset. We use Human-in-the-Loop related techniques and medical guidelines as a source of domain knowledge to establish the importance of the different features that are relevant to establish a pancreatic cancer treatment. These features are not only used as a dimensionality reduction approach for the machine learning models, but also as way to evaluate the explainability capabilities of the different models using agnostic and non-agnostic explainability techniques. To facilitate interpretation of explanatory results, we propose the use of similarity measures such as the Weighted Jaccard Similarity coefficient. The goal is to not only select the best performing model but also the one that can best explain its conclusions and aligns with human domain knowledge.

Published
2024

2. Hybrid Classic-Quantum Computing for Staging of Invasive Ductal Carcinoma of Breast

Author

Moret-Bonillo, Vicente, Mosqueira-Rey, Eduardo, Magaz-Romero, Samuel, and Alvarez-Estevez, Diego

Subjects
Computer Science - Artificial Intelligence
Abstract

Despite the great current relevance of Artificial Intelligence, and the extraordinary innovations that this discipline has brought to many fields -among which, without a doubt, medicine is found-, experts in medical applications of Artificial Intelligence are looking for new alternatives to solve problems for which current Artificial Intelligence programs do not provide with optimal solutions. For this, one promising option could be the use of the concepts and ideas of Quantum Mechanics, for the construction of quantum-based Artificial Intelligence systems. From a hybrid classical-quantum perspective, this article deals with the application of quantum computing techniques for the staging of Invasive Ductal Carcinoma of the breast. It includes: (1) a general explanation of a classical, and well-established, approach for medical reasoning, (2) a description of the clinical problem, (3) a conceptual model for staging invasive ductal carcinoma, (4) some basic notions about Quantum Rule-Based Systems, (5) a step-by-step explanation of the proposed approach for quantum staging of the invasive ductal carcinoma, and (6) the results obtained after running the quantum system on a significant number of use cases. A detailed discussion is also provided at the end of this paper., Comment: Submitted to Information (ISSN 2078-2489)

Published
2023

4. Quantum Factory Method: A Software Engineering Approach to Deal with Incompatibilities in Quantum Libraries

Author

Magaz-Romero, Samuel, Mosqueira-Rey, Eduardo, Alvarez-Estevez, Diego, Moret-Bonillo, Vicente, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Mikyška, Jiří, editor, de Mulatier, Clélia, editor, Paszynski, Maciej, editor, Krzhizhanovskaya, Valeria V., editor, Dongarra, Jack J., editor, and Sloot, Peter M.A., editor

Published
2023
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5. A Convolutional Network for Sleep Stages Classification

Author

Fernández-Varela, Isaac, Hernández-Pereira, Elena, Alvarez-Estevez, Diego, and Moret-Bonillo, Vicente

Subjects
Computer Science - Machine Learning, Quantitative Biology - Neurons and Cognition
Abstract

Sleep stages classification is a crucial task in the context of sleep studies. It involves the simultaneous analysis of multiple signals recorded during sleep. However, it is complex and tedious, and even the trained expert can spend several hours scoring a single night recording. Multiple automatic methods have tried to solve these problems in the past, most of them by classifying a feature vector that is engineered for a specific dataset. In this work, we avoid this bias using a deep learning model that learns relevant features without human intervention. Particularly, we propose an ensemble of 5 convolutional networks that achieves a kappa index of 0.83 when classifying a dataset of 500 sleep recordings., Comment: 6 pages, 3 figures

Published
2019

6. Uncertainty in Quantum Rule-Based Systems

Author

Moret-Bonillo, Vicente, Fernández-Varela, Isaac, and Alvarez-Estevez, Diego

Subjects
Computer Science - Artificial Intelligence, Quantum Physics
Abstract

This article deals with the problem of the uncertainty in rule-based systems (RBS), but from the perspective of quantum computing (QC). In this work we first remember the characteristics of Quantum Rule-Based Systems (QRBS), a concept defined in a previous article by one of the authors of this paper, and we introduce the problem of quantum uncertainty. We assume that the subjective uncertainty that affects the facts of classical RBSs can be treated as a direct consequence of the probabilistic nature of quantum mechanics (QM), and we also assume that the uncertainty associated with a given hypothesis is a consequence of the propagation of the imprecision through the inferential circuits of RBSs. This article does not intend to contribute anything new to the QM field: it is a work of artificial intelligence (AI) that uses QC techniques to solve the problem of uncertainty in RBSs. Bearing the above arguments in mind a quantum model is proposed. This model has been applied to a problem already defined by one of the authors of this work in a previous publication and which is briefly described in this article. Then the model is generalized, and it is thoroughly evaluated. The results obtained show that QC is a valid, effective and efficient method to deal with the inherent uncertainty of RBSs, Comment: 20 pages, 8 tables, 7 figures

Published
2018
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20. Towards the Standardization of Hypnograms Construction for Sleep Analysis

Author

Fernández-Leal, Ángel, Moret-Bonillo, Vicente, Cabrero-Canosa, Mariano J., Magjarevic, Ratko, Editor-in-chief, Ładyżyński, Piotr, Series editor, Ibrahim, Fatimah, Series editor, Lacković, Igor, Series editor, Rock, Emilio Sacristan, Series editor, Kyriacou, Efthyvoulos, editor, Christofides, Stelios, editor, and Pattichis, Constantinos S., editor

Published
2016
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23. Human-in-the-Loop Machine Learning for the Treatment of Pancreatic Cancer

Author

Mosqueira-Rey, Eduardo, primary, Pérez-Sánchez, Alberto, additional, Hernández-Pereira, Elena, additional, Alonso-Ríos, David, additional, Bobes-Bascarán, José, additional, Fernández-Leal, Ángel, additional, Moret-Bonillo, Vicente, additional, Vidal-Ínsua, Yolanda, additional, and Vázquez-Rivera, Francisca, additional

Published
2023
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24. Quantum Factory Method: A Software Engineering Approach to Deal with Incompatibilities in Quantum Libraries

Author

Magaz Romero, Samuel, Mosqueira-Rey, E., Alvarez-Estevez, Diego, Moret-Bonillo, Vicente, Magaz Romero, Samuel, Mosqueira-Rey, E., Alvarez-Estevez, Diego, and Moret-Bonillo, Vicente

Abstract

[Abstract]: The current context of Quantum Computing and its available technologies present an extensive variety of tools and lack of methodologies, leading to incompatibilities across platforms, which end up as inconsistencies in the developed solutions. We propose a design called Quantum Factory Method, based on software engineering and design patterns, to solve these issues by integrating different quantum platforms in the same development. We provide example implementations whose results prove the suitability of the design in different cases, and conclude on how this approach can be expanded for future work.

Published
2023

25. Addressing the data bottleneck in medical deep learning models using a human-in-the-loop machine learning approach

Author

Mosqueira-Rey, E., Hernández-Pereira, Elena, Bobes-Bascarán, José, Alonso Ríos, David, Pérez-Sánchez, Alberto, Fernández-Leal, Ángel, Moret-Bonillo, Vicente, Vidal-Ínsua, Yolanda, Vázquez-Rivera, Francisca, Mosqueira-Rey, E., Hernández-Pereira, Elena, Bobes-Bascarán, José, Alonso Ríos, David, Pérez-Sánchez, Alberto, Fernández-Leal, Ángel, Moret-Bonillo, Vicente, Vidal-Ínsua, Yolanda, and Vázquez-Rivera, Francisca

Abstract

[Abstract]: Any machine learning (ML) model is highly dependent on the data it uses for learning, and this is even more important in the case of deep learning models. The problem is a data bottleneck, i.e. the difficulty in obtaining an adequate number of cases and quality data. Another issue is improving the learning process, which can be done by actively introducing experts into the learning loop, in what is known as human-in-the-loop (HITL) ML. We describe an ML model based on a neural network in which HITL techniques were used to resolve the data bottleneck problem for the treatment of pancreatic cancer. We first augmented the dataset using synthetic cases created by a generative adversarial network. We then launched an active learning (AL) process involving human experts as oracles to label both new cases and cases by the network found to be suspect. This AL process was carried out simultaneously with an interactive ML process in which feedback was obtained from humans in order to develop better synthetic cases for each iteration of training. We discuss the challenges involved in including humans in the learning process, especially in relation to human–computer interaction, which is acquiring great importance in building ML models and can condition the success of a HITL approach. This paper also discusses the methodological approach adopted to address these challenges.

Published
2023

26. Understanding Machine Learning Explainability Models in the context of Pancreatic Cancer Treatment

Author

Bobes-Bascarán, José, Fernández-Leal, Ángel, Mosqueira-Rey, E., Alonso Ríos, David, Hernández-Pereira, Elena, Moret-Bonillo, Vicente, Bobes-Bascarán, José, Fernández-Leal, Ángel, Mosqueira-Rey, E., Alonso Ríos, David, Hernández-Pereira, Elena, and Moret-Bonillo, Vicente

Abstract

[Abstract] The increasing adoption of artificial intelligent systems at sensitive domains where humans are particularly, such as medicine, has provided the context to deeply explore ways of making machine learning models (ML) understandable for their final users. The success of such systems require the trust of their users, and thus there is a need to design and provide methods to understand the decisions made by such systems. We start from a public Pancreatic Cancer dataset and experiment with different ML models on a diagnosis scenario with the goal to decide whether a patient should be prescribed with a chemotherapy treatment. To validate the diagnosis results we explore different explainability approaches: Decision Tree, Random Forest, and model agnostic ad-hoc models, and compare them against a standard Pancreatic Cancer treatment set of rules. The increasing adoption of artificial intelligent systems at sensitive domains where humans are particularly, such as medicine, has provided the context to deeply explore ways of making machine learning models (ML) understandable for their final users. The success of such systems require the trust of their users, and thus there is a need to design and provide methods to understand the decisions made by such systems. We start from a public Pancreatic Cancer dataset and experiment with different ML models. To validate the diagnostic results we explore different explainability approaches: Decision Tree based approach, Random Forest based approach, and different model agnostic ad-hoc approaches, and we compare them against a standard Pancreatic Cancer treatment set of rules

Published
2023

29. D6.11 Preliminary QRBS software and IDC application specification

Author

Moret-Bonillo, Vicente, Gomez Tato, Andres, Magaz-Romero, Samuel, Mosqueira-Rey, Eduardo, and Alvarez-Estevez, Diego

Subjects
IDC application, QRBS, software implementation
Abstract

This report is the fourth deliverable of Task 6.2 – Quantum Rule-Based Systems (QRBS) for breast cancer detection of the NEASQC project. The document presents the work carried out so far, and is complementary to the other deliverables of this task. D6.11 (M32) will include a preliminary version of the RBS and QRBS software along with the specification of the IDC application that will be developed. The report begins with an introduction of the preliminary version of the QRBS software, presenting how the project is structured, providing some use examples of common operations users will make with the library, and a brief com-mentary on the library’s documentation which is presented in an appendix. Following that, we present the specification for the Invasive Ductal Carcinoma (IDC) application, based on previous work on quantum computing techniques and applying them to the clinical problem specifically. We continue by extending the work on previous deliverables regarding traceability and testing of the QRBS software, which is a critical part on making sure that the developed library provides valid and verified functionalities. To close the report, the conclusions obtained during the development of the work carried out are presented and some ideas for future work to be included in upcoming deliverables are shown., {"references":["Amin, M. B., Greene, F. L., Edge, S. B., Compton, C. C., Gershenwald, J. E., Brookland, R. K., Meyer, L., Gress, D. M., Byrd, D. R., & Winchester, D. P. (2017). The eighth edition ajcc cancer staging manual: Continuing to build a bridge from a population-based to a more \"personalized\" approach to cancer staging. CA: A Cancer Journal for Clinicians, 67(2), 93–99. https://doi.org/10.3322/caac.21388","Giuliano, A. E., Edge, S. B., & Hortobagyi, G. N. (2018). Eighth edition of the ajcc cancer staging manual: Breast cancer. Annals of Surgical Oncology, 25(7), 1783–1785. https://doi.org/10.1245/s10434-018-6486-6","Kruchten, P. (2004). The rational unified process: An introduction. Addison-Wesley.","Ledley, R. S., & Lusted, L. B. (1959). Reasoning foundations of medical diagnosis. Science, 130(3366), 9–21. https: //doi.org/10.1126/science.130.3366.9","Moret-Bonillo, V., Gomez Tato, A., Magaz Romero, S., Mosqueira-Rey, E., & Alvarez-Estevez, D. (2022). D6.9: Qrbs software specifications. https://doi.org/10.5281/zenodo.7299193","Moret-Bonillo, V., Magaz-Romero, S., & Mosqueira-Rey, E. (2022). Quantum computing for dealing with inaccurate knowledge related to the certainty factors model. Mathematics, 10(2). https://doi.org/10.3390/math10020189","Moret-Bonillo, V., Mosqueira-Rey, E., & Magaz-Romero, S. (2021). D6.5 Quantum Rule-Based System (QRBS) Requirement Analysis. https://doi.org/10.5281/zenodo.5949157","Moret-Bonillo, V., Mosqueira-Rey, E., Magaz-Romero, S., & Alvarez-Estevez, D. (2023). Hybrid classic-quantum computing for staging of invasive ductal carcinoma of breast. arXiv preprint arXiv:2303.10142. https://arxiv. org/abs/2303.10142","Moret-Bonillo, V., Mosqueira-Rey, E., Magaz-Romero, S., & G´omez-Tato, A. (2021). Quantum rule-based systems (qrbs) models, architecture and formal specification (D6. 2). https://www.neasqc.eu/wp- content/uploads/ 2021/05/NEASQC D6.2 QRBS-Models-Architecture-and-Formal-Specification-V1.5-Final.pdf","Waterman, D. A. (1985). A guide to expert systems. Addison-Wesley Longman Publishing Co., Inc."]}

Published
2023
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31. D6.9: QRBS software specifications

Author

Moret-Bonillo, Vicente, Gomez Tato, Andres, Magaz Romero, Samuel, Mosqueira-Rey, Eduardo, and Alvarez-Estevez, Diego

Subjects
dynamic design, software specifications, static design, testing
Abstract

This report is the third deliverable of Task 6.2 - Quantum Rule-Based Systems (QRBS) for breast cancer detection of the NEASQC project. The document presents the work carried out so far, and is complementary to the other deliverables of this task. The report begins with an introduction into the software specification process, presenting the necessary definitions for the reader to comprehend the work carried out. Following those concepts come the several specifications obtained, including both static and dynamic design, as well as the implementations specifications for this use case. Following that, focus is on the testing phase, in order to prove that the specification obtained is coherent regards both itself and the previous work. To close the report, the conclusions obtained during the development of the work carried out are presented to enhance the following work., {"references":["Ambler, S. (2002).Agile modeling: Effective practices for extreme programming and the unified process. John Wiley & Sons.","Bourque, P., & Fairley, R. E. (2014).Guide to the software engineering body of knowledge (SWEBOK©). IEEE Computer Society Press.","Kruchten, P. (2004).The rational unified process: An introduction. Addison-Wesley.","Pressman, R. S. (2005).Software engineering: A practitioner's approach. Palgrave macmillan"]}

Published
2022
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37. Interpretation of Group Measurements of Validation Data Using Fuzzy Techniques in an Object-Oriented Approach

Author

Mosqueira-Rey, Eduardo, Moret-Bonillo, Vicente, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Dough, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Carbonell, Jaime G., editor, Siekmann, Jörg, editor, Gabrys, Bogdan, editor, Howlett, Robert J., editor, and Jain, Lakhmi C., editor

Published
2006
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40. A Bayesian Neural Network Approach for Sleep Apnea Classification

Author

Fontenla-Romero, Oscar, Guijarro-Berdiñas, Bertha, Alonso-Betanzos, Amparo, del Rocío Fraga-Iglesias, Ana, Moret-Bonillo, Vicente, Goos, Gerhard, editor, Hartmanis, Juris, editor, van Leeuwen, Jan, editor, Carbonell, Jaime G., editor, Siekmann, Jörg, editor, Dojat, Michel, editor, Keravnou, Elpida T., editor, and Barahona, Pedro, editor

Published
2003
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41. Quantum Computing for Dealing with Inaccurate Knowledge Related to the Certainty Factors Model

Author

Moret-Bonillo, Vicente, Magaz Romero, Samuel, Mosqueira-Rey, E., Moret-Bonillo, Vicente, Magaz Romero, Samuel, and Mosqueira-Rey, E.

Abstract

[Abstract] In this paper, we illustrate that inaccurate knowledge can be efficiently implemented in a quantum environment. For this purpose, we analyse the correlation between certainty factors and quantum probability. We first explore the certainty factors approach for inexact reasoning from a classical point of view. Next, we introduce some basic aspects of quantum computing, and we pay special attention to quantum rule-based systems. In this context, a specific use case was built: an inferential network for testing the behaviour of the certainty factors approach in a quantum environment. After the design and execution of the experiments, the corresponding analysis of the obtained results was performed in three different scenarios: (1) inaccuracy in declarative knowledge, or imprecision, (2) inaccuracy in procedural knowledge, or uncertainty, and (3) inaccuracy in both declarative and procedural knowledge. This paper, as stated in the conclusions, is intended to pave the way for future quantum implementations of well-established methods for handling inaccurate knowledge.

Published
2022

48. D6.5 Quantum Rule-Based System (QRBS) Requirement Analysis

Author

Moret-Bonillo, Vicente, Mosqueira-Rey, Eduardo, Magaz-Romero, Samuel, Hibti, Mohamed, Laarman, Alfons, and Gómez, Andrés

Abstract

This report is the second deliverable of Task 6.2-Quantum Rule-Based Systems (QRBS) for breast cancer detection of the NEASQC project.The document presents the work carried out so far, and is complementary to the other deliverables of this task. The report begins with an introduction into the requirement analysis process, explaining the necessary concepts to understand the development of the work. Along with said concepts,the applied procedure for the analysis of requirements used in this work is also presented, to put the reader in context and to justify the following sections. Once the necessary concepts have been presented, the report illustrates the different phases of the work carried out, starting with the needs and features (vision).This phase serves to detect several factors that are crucial to the requirement analysis, such as the actors related to the system or the needs that the system must cover. The document continues with next step of the process, related to the use cases, which are decompositions of the previous needs according to their functionality and logical structuring. Each use case is studied, explained and detailed in depth. This decomposition allows for a more robust and traceable development across the next phases of the work. Finally, following the previous decomposition, test cases are defined. This test cases conform a test plan that will help on later stages of the development,when it arises the need to check whether the system meets the requirements specified., {"references":["Adolph, S., Bramble, P., Cockburn, A., & Pols, A. (2003). Patterns for Effective Use Cases.Pearson Education, Inc.","Ambler, S. (2002). Agile Modeling: Effective Practices for Extreme Programming and the Unified Process.John Wiley & Sons.","Cockburn, A. (2001). Writing Effective Use Cases.Addison-Wesley.","Hasan, K., Ramsay, B., & Moyes, I. (1994).Object oriented expert systems for real-time power system alarm processing: Part I. Selection of a toolkit. Electric Power Systems Research, 30(1), 69-75.","ISO/IEC/IEEE. (2010). ISO/IEC/IEEE International Standard -Systems and software engineering --Vocabulary. ISO/IEC/IEEE 24765:2010(E), 1-41","ISO/IEC/IEEE. (2017). ISO/IEC/IEEE International Standard -Systems and software engineering -Vocabulary.ISO/IEC/IEEE. doi:10.1109/IEEESTD.2017.8016712","Kotonya, G., & Sommerville, I. (1998). Requirements Engineering: Processes and Techniques.John Wiley & Sons.","Krause, P., & Clark, D. (2012). Representing uncertain knowledge: an artificial intelligence approach.Springer Science & Business Media.","Kruchten, P. (2004). The Rational Unified Process: An Introduction(3rd ed.). Addison-Wesley Professional.","Larman, C. (2005). Applying UML and Patterns: An Introduction to Object-Oriented Analysis and Design and Iterative Development(3rd ed.). Pearson Education, Inc.","Martin, R. C. (2000). Design principles and design patterns. Object Mentor, 1(34), 597.","Perot, F. (1988). A knowledge acquisition assistant for the expert system shell Nexpert-Object.","Prusak, L. (2001). Where did knowledge management come from? IBM Systems Journal, 40(4), 1002-1007.","Rumbaugh, J.,Jacobson, I., & Booch, G. (2004). Unified Modeling Language Reference Manual, The (2nd Edition).Pearson Higher Education.","Spence, I., & Probasco, L. (2001). Traceability Strategies for Managing Requirements with Use Cases. Rational Software White Paper. Recuperado el 13 de 12 de 2021, de https://www.semanticscholar.org/paper/Traceability-Strategies-for-Managing-Requirements-Spence-Probasco/4ed79d38b02b070b165ab91267a7390b89903fc6","Rehman, T., Khan, M. N., & Riaz, N. (2013). Analysis of requirement engineering processes, tools/techniques and methodologies. International Journal of Information Technology and Computer Science (IJITCS), 5(3), 40."]}

Published
2021
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