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Towards the Interpretability of Machine Learning Predictions for Medical Applications Targeting Personalised Therapies: A Cancer Case Survey

Artificial Intelligence is providing astonishing results, with medicine being one of its favourite playgrounds. Machine Learning and, in particular, Deep Neural Networks are behind this revolution. Among the most challenging targets of interest in medicine are cancer diagnosis and therapies but, to...

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Detalles Bibliográficos
Autores principales: Banegas-Luna, Antonio Jesús, Peña-García, Jorge, Iftene, Adrian, Guadagni, Fiorella, Ferroni, Patrizia, Scarpato, Noemi, Zanzotto, Fabio Massimo, Bueno-Crespo, Andrés, Pérez-Sánchez, Horacio
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8122817/
https://www.ncbi.nlm.nih.gov/pubmed/33922356
http://dx.doi.org/10.3390/ijms22094394
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author Banegas-Luna, Antonio Jesús
Peña-García, Jorge
Iftene, Adrian
Guadagni, Fiorella
Ferroni, Patrizia
Scarpato, Noemi
Zanzotto, Fabio Massimo
Bueno-Crespo, Andrés
Pérez-Sánchez, Horacio
author_facet Banegas-Luna, Antonio Jesús
Peña-García, Jorge
Iftene, Adrian
Guadagni, Fiorella
Ferroni, Patrizia
Scarpato, Noemi
Zanzotto, Fabio Massimo
Bueno-Crespo, Andrés
Pérez-Sánchez, Horacio
author_sort Banegas-Luna, Antonio Jesús
collection PubMed
description Artificial Intelligence is providing astonishing results, with medicine being one of its favourite playgrounds. Machine Learning and, in particular, Deep Neural Networks are behind this revolution. Among the most challenging targets of interest in medicine are cancer diagnosis and therapies but, to start this revolution, software tools need to be adapted to cover the new requirements. In this sense, learning tools are becoming a commodity but, to be able to assist doctors on a daily basis, it is essential to fully understand how models can be interpreted. In this survey, we analyse current machine learning models and other in-silico tools as applied to medicine—specifically, to cancer research—and we discuss their interpretability, performance and the input data they are fed with. Artificial neural networks (ANN), logistic regression (LR) and support vector machines (SVM) have been observed to be the preferred models. In addition, convolutional neural networks (CNNs), supported by the rapid development of graphic processing units (GPUs) and high-performance computing (HPC) infrastructures, are gaining importance when image processing is feasible. However, the interpretability of machine learning predictions so that doctors can understand them, trust them and gain useful insights for the clinical practice is still rarely considered, which is a factor that needs to be improved to enhance doctors’ predictive capacity and achieve individualised therapies in the near future.
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spelling pubmed-81228172021-05-16 Towards the Interpretability of Machine Learning Predictions for Medical Applications Targeting Personalised Therapies: A Cancer Case Survey Banegas-Luna, Antonio Jesús Peña-García, Jorge Iftene, Adrian Guadagni, Fiorella Ferroni, Patrizia Scarpato, Noemi Zanzotto, Fabio Massimo Bueno-Crespo, Andrés Pérez-Sánchez, Horacio Int J Mol Sci Review Artificial Intelligence is providing astonishing results, with medicine being one of its favourite playgrounds. Machine Learning and, in particular, Deep Neural Networks are behind this revolution. Among the most challenging targets of interest in medicine are cancer diagnosis and therapies but, to start this revolution, software tools need to be adapted to cover the new requirements. In this sense, learning tools are becoming a commodity but, to be able to assist doctors on a daily basis, it is essential to fully understand how models can be interpreted. In this survey, we analyse current machine learning models and other in-silico tools as applied to medicine—specifically, to cancer research—and we discuss their interpretability, performance and the input data they are fed with. Artificial neural networks (ANN), logistic regression (LR) and support vector machines (SVM) have been observed to be the preferred models. In addition, convolutional neural networks (CNNs), supported by the rapid development of graphic processing units (GPUs) and high-performance computing (HPC) infrastructures, are gaining importance when image processing is feasible. However, the interpretability of machine learning predictions so that doctors can understand them, trust them and gain useful insights for the clinical practice is still rarely considered, which is a factor that needs to be improved to enhance doctors’ predictive capacity and achieve individualised therapies in the near future. MDPI 2021-04-22 /pmc/articles/PMC8122817/ /pubmed/33922356 http://dx.doi.org/10.3390/ijms22094394 Text en © 2021 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Review
Banegas-Luna, Antonio Jesús
Peña-García, Jorge
Iftene, Adrian
Guadagni, Fiorella
Ferroni, Patrizia
Scarpato, Noemi
Zanzotto, Fabio Massimo
Bueno-Crespo, Andrés
Pérez-Sánchez, Horacio
Towards the Interpretability of Machine Learning Predictions for Medical Applications Targeting Personalised Therapies: A Cancer Case Survey
title Towards the Interpretability of Machine Learning Predictions for Medical Applications Targeting Personalised Therapies: A Cancer Case Survey
title_full Towards the Interpretability of Machine Learning Predictions for Medical Applications Targeting Personalised Therapies: A Cancer Case Survey
title_fullStr Towards the Interpretability of Machine Learning Predictions for Medical Applications Targeting Personalised Therapies: A Cancer Case Survey
title_full_unstemmed Towards the Interpretability of Machine Learning Predictions for Medical Applications Targeting Personalised Therapies: A Cancer Case Survey
title_short Towards the Interpretability of Machine Learning Predictions for Medical Applications Targeting Personalised Therapies: A Cancer Case Survey
title_sort towards the interpretability of machine learning predictions for medical applications targeting personalised therapies: a cancer case survey
topic Review
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8122817/
https://www.ncbi.nlm.nih.gov/pubmed/33922356
http://dx.doi.org/10.3390/ijms22094394
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