Cargando…

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...

Descripción completa

Detalles Bibliográficos
Autores principales: Vaccari, Ivan, Orani, Vanessa, Paglialonga, Alessia, Cambiaso, Enrico, Mongelli, Maurizio
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
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
_version_ 1783706997095399424
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
work_keys_str_mv AT vaccariivan agenerativeadversarialnetworkgantechniqueforinternetofmedicalthingsdata
AT oranivanessa agenerativeadversarialnetworkgantechniqueforinternetofmedicalthingsdata
AT paglialongaalessia agenerativeadversarialnetworkgantechniqueforinternetofmedicalthingsdata
AT cambiasoenrico agenerativeadversarialnetworkgantechniqueforinternetofmedicalthingsdata
AT mongellimaurizio agenerativeadversarialnetworkgantechniqueforinternetofmedicalthingsdata
AT vaccariivan generativeadversarialnetworkgantechniqueforinternetofmedicalthingsdata
AT oranivanessa generativeadversarialnetworkgantechniqueforinternetofmedicalthingsdata
AT paglialongaalessia generativeadversarialnetworkgantechniqueforinternetofmedicalthingsdata
AT cambiasoenrico generativeadversarialnetworkgantechniqueforinternetofmedicalthingsdata
AT mongellimaurizio generativeadversarialnetworkgantechniqueforinternetofmedicalthingsdata