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A Generative Adversarial Network (GAN) Technique for Internet of Medical Things Data
The application of machine learning and artificial intelligence techniques in the medical world is growing, with a range of purposes: from the identification and prediction of possible diseases to patient monitoring and clinical decision support systems. Furthermore, the widespread use of remote mon...
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/PMC8197837/ https://www.ncbi.nlm.nih.gov/pubmed/34071944 http://dx.doi.org/10.3390/s21113726 |
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author | Vaccari, Ivan Orani, Vanessa Paglialonga, Alessia Cambiaso, Enrico Mongelli, Maurizio |
author_facet | Vaccari, Ivan Orani, Vanessa Paglialonga, Alessia Cambiaso, Enrico Mongelli, Maurizio |
author_sort | Vaccari, Ivan |
collection | PubMed |
description | The application of machine learning and artificial intelligence techniques in the medical world is growing, with a range of purposes: from the identification and prediction of possible diseases to patient monitoring and clinical decision support systems. Furthermore, the widespread use of remote monitoring medical devices, under the umbrella of the “Internet of Medical Things” (IoMT), has simplified the retrieval of patient information as they allow continuous monitoring and direct access to data by healthcare providers. However, due to possible issues in real-world settings, such as loss of connectivity, irregular use, misuse, or poor adherence to a monitoring program, the data collected might not be sufficient to implement accurate algorithms. For this reason, data augmentation techniques can be used to create synthetic datasets sufficiently large to train machine learning models. In this work, we apply the concept of generative adversarial networks (GANs) to perform a data augmentation from patient data obtained through IoMT sensors for Chronic Obstructive Pulmonary Disease (COPD) monitoring. We also apply an explainable AI algorithm to demonstrate the accuracy of the synthetic data by comparing it to the real data recorded by the sensors. The results obtained demonstrate how synthetic datasets created through a well-structured GAN are comparable with a real dataset, as validated by a novel approach based on machine learning. |
format | Online Article Text |
id | pubmed-8197837 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-81978372021-06-14 A Generative Adversarial Network (GAN) Technique for Internet of Medical Things Data Vaccari, Ivan Orani, Vanessa Paglialonga, Alessia Cambiaso, Enrico Mongelli, Maurizio Sensors (Basel) Article The application of machine learning and artificial intelligence techniques in the medical world is growing, with a range of purposes: from the identification and prediction of possible diseases to patient monitoring and clinical decision support systems. Furthermore, the widespread use of remote monitoring medical devices, under the umbrella of the “Internet of Medical Things” (IoMT), has simplified the retrieval of patient information as they allow continuous monitoring and direct access to data by healthcare providers. However, due to possible issues in real-world settings, such as loss of connectivity, irregular use, misuse, or poor adherence to a monitoring program, the data collected might not be sufficient to implement accurate algorithms. For this reason, data augmentation techniques can be used to create synthetic datasets sufficiently large to train machine learning models. In this work, we apply the concept of generative adversarial networks (GANs) to perform a data augmentation from patient data obtained through IoMT sensors for Chronic Obstructive Pulmonary Disease (COPD) monitoring. We also apply an explainable AI algorithm to demonstrate the accuracy of the synthetic data by comparing it to the real data recorded by the sensors. The results obtained demonstrate how synthetic datasets created through a well-structured GAN are comparable with a real dataset, as validated by a novel approach based on machine learning. MDPI 2021-05-27 /pmc/articles/PMC8197837/ /pubmed/34071944 http://dx.doi.org/10.3390/s21113726 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 Vaccari, Ivan Orani, Vanessa Paglialonga, Alessia Cambiaso, Enrico Mongelli, Maurizio A Generative Adversarial Network (GAN) Technique for Internet of Medical Things Data |
title | A Generative Adversarial Network (GAN) Technique for Internet of Medical Things Data |
title_full | A Generative Adversarial Network (GAN) Technique for Internet of Medical Things Data |
title_fullStr | A Generative Adversarial Network (GAN) Technique for Internet of Medical Things Data |
title_full_unstemmed | A Generative Adversarial Network (GAN) Technique for Internet of Medical Things Data |
title_short | A Generative Adversarial Network (GAN) Technique for Internet of Medical Things Data |
title_sort | generative adversarial network (gan) technique for internet of medical things data |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8197837/ https://www.ncbi.nlm.nih.gov/pubmed/34071944 http://dx.doi.org/10.3390/s21113726 |
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