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Occlusion-Based Explanations in Deep Recurrent Models for Biomedical Signals
The biomedical field is characterized by an ever-increasing production of sequential data, which often come in the form of biosignals capturing the time-evolution of physiological processes, such as blood pressure and brain activity. This has motivated a large body of research dealing with the devel...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8394492/ https://www.ncbi.nlm.nih.gov/pubmed/34441204 http://dx.doi.org/10.3390/e23081064 |
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author | Resta, Michele Monreale, Anna Bacciu, Davide |
author_facet | Resta, Michele Monreale, Anna Bacciu, Davide |
author_sort | Resta, Michele |
collection | PubMed |
description | The biomedical field is characterized by an ever-increasing production of sequential data, which often come in the form of biosignals capturing the time-evolution of physiological processes, such as blood pressure and brain activity. This has motivated a large body of research dealing with the development of machine learning techniques for the predictive analysis of such biosignals. Unfortunately, in high-stakes decision making, such as clinical diagnosis, the opacity of machine learning models becomes a crucial aspect to be addressed in order to increase the trust and adoption of AI technology. In this paper, we propose a model agnostic explanation method, based on occlusion, that enables the learning of the input’s influence on the model predictions. We specifically target problems involving the predictive analysis of time-series data and the models that are typically used to deal with data of such nature, i.e., recurrent neural networks. Our approach is able to provide two different kinds of explanations: one suitable for technical experts, who need to verify the quality and correctness of machine learning models, and one suited to physicians, who need to understand the rationale underlying the prediction to make aware decisions. A wide experimentation on different physiological data demonstrates the effectiveness of our approach both in classification and regression tasks. |
format | Online Article Text |
id | pubmed-8394492 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-83944922021-08-28 Occlusion-Based Explanations in Deep Recurrent Models for Biomedical Signals Resta, Michele Monreale, Anna Bacciu, Davide Entropy (Basel) Article The biomedical field is characterized by an ever-increasing production of sequential data, which often come in the form of biosignals capturing the time-evolution of physiological processes, such as blood pressure and brain activity. This has motivated a large body of research dealing with the development of machine learning techniques for the predictive analysis of such biosignals. Unfortunately, in high-stakes decision making, such as clinical diagnosis, the opacity of machine learning models becomes a crucial aspect to be addressed in order to increase the trust and adoption of AI technology. In this paper, we propose a model agnostic explanation method, based on occlusion, that enables the learning of the input’s influence on the model predictions. We specifically target problems involving the predictive analysis of time-series data and the models that are typically used to deal with data of such nature, i.e., recurrent neural networks. Our approach is able to provide two different kinds of explanations: one suitable for technical experts, who need to verify the quality and correctness of machine learning models, and one suited to physicians, who need to understand the rationale underlying the prediction to make aware decisions. A wide experimentation on different physiological data demonstrates the effectiveness of our approach both in classification and regression tasks. MDPI 2021-08-17 /pmc/articles/PMC8394492/ /pubmed/34441204 http://dx.doi.org/10.3390/e23081064 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 | Article Resta, Michele Monreale, Anna Bacciu, Davide Occlusion-Based Explanations in Deep Recurrent Models for Biomedical Signals |
title | Occlusion-Based Explanations in Deep Recurrent Models for Biomedical Signals |
title_full | Occlusion-Based Explanations in Deep Recurrent Models for Biomedical Signals |
title_fullStr | Occlusion-Based Explanations in Deep Recurrent Models for Biomedical Signals |
title_full_unstemmed | Occlusion-Based Explanations in Deep Recurrent Models for Biomedical Signals |
title_short | Occlusion-Based Explanations in Deep Recurrent Models for Biomedical Signals |
title_sort | occlusion-based explanations in deep recurrent models for biomedical signals |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8394492/ https://www.ncbi.nlm.nih.gov/pubmed/34441204 http://dx.doi.org/10.3390/e23081064 |
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