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The importance of interpretability and visualization in machine learning for applications in medicine and health care

Neural computing & applications, 2020-12, Vol.32 (24), p.18069-18083

Vellido, Alfredo

2020

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Societal Issues Concerning the Application of Artificial Intelligence in Medicine

Kidney diseases, 2019-02, Vol.5 (1), p.11-17

Vellido, Alfredo

2019

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Intelligent data analysis approaches to churn as a business problem: a survey

Knowledge and information systems, 2017-06, Vol.51 (3), p.719-774

2017

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Machine learning in critical care: state-of-the-art and a sepsis case study

Biomedical engineering online, 2018-11, Vol.17 (Suppl 1), p.135-135, Article 135

2018

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Feature selection for the accurate prediction of septic and cardiogenic shock ICU mortality in the acute phase

PloS one, 2018-11, Vol.13 (11), p.e0199089-e0199089

2018

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Using random forests for assistance in the curation of G-protein coupled receptor databases

Biomedical engineering online, 2017-08, Vol.16 (Suppl 1), p.75-75, Article 75

2017

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Blood Pressure Assessment with Differential Pulse Transit Time and Deep Learning: A Proof of Concept

Kidney diseases, 2019-02, Vol.5 (1), p.23-27

2019

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Automated classification of brain tumours from short echo time in vivo MRS data using Gaussian Decomposition and Bayesian Neural Networks

Expert systems with applications, 2014-09, Vol.41 (11), p.5296-5307

2014

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Using machine learning tools for protein database biocuration assistance

Scientific reports, 2018-07, Vol.8 (1), p.10148-10, Article 10148

2018

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Label noise in subtype discrimination of class C G protein-coupled receptors: A systematic approach to the analysis of classification errors

BMC bioinformatics, 2015-09, Vol.16 (1), p.314-314, Article 314

2015

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Discriminant Convex Non-negative Matrix Factorization for the classification of human brain tumours

Pattern recognition letters, 2013-10, Vol.34 (14), p.1734-1747

2013

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Convex non-negative matrix factorization for brain tumor delimitation from MRSI data

PloS one, 2012-10, Vol.7 (10), p.e47824-e47824

2012

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Advances in clustering and visualization of time series using GTM through time

Neural networks, 2008-09, Vol.21 (7), p.904-913

2008

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Making nonlinear manifold learning models interpretable: The manifold grand tour

Expert systems with applications, 2015-12, Vol.42 (22), p.8982-8988

2015

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Sepsis mortality prediction with the Quotient Basis Kernel

Artificial intelligence in medicine, 2014-05, Vol.61 (1), p.45-52

2014

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Systematic Analysis of Primary Sequence Domain Segments for the Discrimination Between Class C GPCR Subtypes

Interdisciplinary sciences : computational life sciences, 2018-03, Vol.10 (1), p.43-52

2018

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Artificial Intelligence for the Artificial Kidney: Pointers to the Future of a Personalized Hemodialysis Therapy

Kidney diseases, 2018-02, Vol.4 (1), p.1-9

2018

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Influencing factors in energy use of housing blocks: a new methodology, based on clustering and energy simulations, for decision making in energy refurbishment projects

Energy efficiency, 2017-04, Vol.10 (2), p.359-382

2017

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The influence of alignment-free sequence representations on the semi-supervised classification of class C G protein-coupled receptors

2015

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Advances in machine learning and computational intelligence

Neurocomputing (Amsterdam), 2009-03, Vol.72 (7), p.1377-1378

2009

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Reducing the n-gram feature space of class C GPCRs to subtype-discriminating patterns

Journal of integrative bioinformatics, 2014-10, Vol.11 (3), p.99-115

2014

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Visual interpretation of class C GPCR subtype overlapping from the nonlinear mapping of transformed primary sequences

IEEE-EMBS International Conference on Biomedical and Health Informatics (BHI), 2014, p.764-767

2014

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Visual Characterization of Misclassified Class C GPCRs through Manifold-based Machine Learning Methods

Genomics and computational biology, 2015-09, Vol.1 (1), p.19

2015

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Variational Bayesian Generative Topographic Mapping

Journal of mathematical modelling and algorithms, 2008-12, Vol.7 (4), p.371-387

2008

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Automated Quality Control for Proton Magnetic Resonance Spectroscopy Data Using Convex Non-negative Matrix Factorization

Bioinformatics and Biomedical Engineering, 2016, p.719-727

2016

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The importance of interpretability and visualization in machine learning for applications in medicine and health care

Societal Issues Concerning the Application of Artificial Intelligence in Medicine

Intelligent data analysis approaches to churn as a business problem: a survey

Machine learning in critical care: state-of-the-art and a sepsis case study

Feature selection for the accurate prediction of septic and cardiogenic shock ICU mortality in the acute phase

Using random forests for assistance in the curation of G-protein coupled receptor databases

Blood Pressure Assessment with Differential Pulse Transit Time and Deep Learning: A Proof of Concept

Automated classification of brain tumours from short echo time in vivo MRS data using Gaussian Decomposition and Bayesian Neural Networks

Using machine learning tools for protein database biocuration assistance

Label noise in subtype discrimination of class C G protein-coupled receptors: A systematic approach to the analysis of classification errors

Discriminant Convex Non-negative Matrix Factorization for the classification of human brain tumours

Convex non-negative matrix factorization for brain tumor delimitation from MRSI data

Advances in clustering and visualization of time series using GTM through time

Making nonlinear manifold learning models interpretable: The manifold grand tour

Sepsis mortality prediction with the Quotient Basis Kernel

Systematic Analysis of Primary Sequence Domain Segments for the Discrimination Between Class C GPCR Subtypes

Artificial Intelligence for the Artificial Kidney: Pointers to the Future of a Personalized Hemodialysis Therapy

Influencing factors in energy use of housing blocks: a new methodology, based on clustering and energy simulations, for decision making in energy refurbishment projects

The influence of alignment-free sequence representations on the semi-supervised classification of class C G protein-coupled receptors

Advances in machine learning and computational intelligence

Reducing the n-gram feature space of class C GPCRs to subtype-discriminating patterns

Visual interpretation of class C GPCR subtype overlapping from the nonlinear mapping of transformed primary sequences

Visual Characterization of Misclassified Class C GPCRs through Manifold-based Machine Learning Methods

Variational Bayesian Generative Topographic Mapping

Automated Quality Control for Proton Magnetic Resonance Spectroscopy Data Using Convex Non-negative Matrix Factorization

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