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Computer Aided Detection System for Prediction of the Malaise during Hemodialysis

Monitoring of dialysis sessions is crucial as different stress factors can yield suffering or critical situations. Specialized personnel is usually required for the administration of this medical treatment; nevertheless, subjects whose clinical status can be considered stable require different monit...

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Autores principales: Tangaro, Sabina, Fanizzi, Annarita, Amoroso, Nicola, Corciulo, Roberto, Garuccio, Elena, Gesualdo, Loreto, Loizzo, Giuliana, Procaccini, Deni Aldo, Vernò, Lucia, Bellotti, Roberto
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Hindawi Publishing Corporation 2016
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4799825/
https://www.ncbi.nlm.nih.gov/pubmed/27042200
http://dx.doi.org/10.1155/2016/8748156
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author Tangaro, Sabina
Fanizzi, Annarita
Amoroso, Nicola
Corciulo, Roberto
Garuccio, Elena
Gesualdo, Loreto
Loizzo, Giuliana
Procaccini, Deni Aldo
Vernò, Lucia
Bellotti, Roberto
author_facet Tangaro, Sabina
Fanizzi, Annarita
Amoroso, Nicola
Corciulo, Roberto
Garuccio, Elena
Gesualdo, Loreto
Loizzo, Giuliana
Procaccini, Deni Aldo
Vernò, Lucia
Bellotti, Roberto
author_sort Tangaro, Sabina
collection PubMed
description Monitoring of dialysis sessions is crucial as different stress factors can yield suffering or critical situations. Specialized personnel is usually required for the administration of this medical treatment; nevertheless, subjects whose clinical status can be considered stable require different monitoring strategies when compared with subjects with critical clinical conditions. In this case domiciliary treatment or monitoring can substantially improve the quality of life of patients undergoing dialysis. In this work, we present a Computer Aided Detection (CAD) system for the telemonitoring of patients' clinical parameters. The CAD was mainly designed to predict the insurgence of critical events; it consisted of two Random Forest (RF) classifiers: the first one (RF(1)) predicting the onset of any malaise one hour after the treatment start and the second one (RF(2)) again two hours later. The developed system shows an accurate classification performance in terms of both sensitivity and specificity. The specificity in the identification of nonsymptomatic sessions and the sensitivity in the identification of symptomatic sessions for RF(2) are equal to 86.60% and 71.40%, respectively, thus suggesting the CAD as an effective tool to support expert nephrologists in telemonitoring the patients.
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spelling pubmed-47998252016-04-03 Computer Aided Detection System for Prediction of the Malaise during Hemodialysis Tangaro, Sabina Fanizzi, Annarita Amoroso, Nicola Corciulo, Roberto Garuccio, Elena Gesualdo, Loreto Loizzo, Giuliana Procaccini, Deni Aldo Vernò, Lucia Bellotti, Roberto Comput Math Methods Med Research Article Monitoring of dialysis sessions is crucial as different stress factors can yield suffering or critical situations. Specialized personnel is usually required for the administration of this medical treatment; nevertheless, subjects whose clinical status can be considered stable require different monitoring strategies when compared with subjects with critical clinical conditions. In this case domiciliary treatment or monitoring can substantially improve the quality of life of patients undergoing dialysis. In this work, we present a Computer Aided Detection (CAD) system for the telemonitoring of patients' clinical parameters. The CAD was mainly designed to predict the insurgence of critical events; it consisted of two Random Forest (RF) classifiers: the first one (RF(1)) predicting the onset of any malaise one hour after the treatment start and the second one (RF(2)) again two hours later. The developed system shows an accurate classification performance in terms of both sensitivity and specificity. The specificity in the identification of nonsymptomatic sessions and the sensitivity in the identification of symptomatic sessions for RF(2) are equal to 86.60% and 71.40%, respectively, thus suggesting the CAD as an effective tool to support expert nephrologists in telemonitoring the patients. Hindawi Publishing Corporation 2016 2016-03-06 /pmc/articles/PMC4799825/ /pubmed/27042200 http://dx.doi.org/10.1155/2016/8748156 Text en Copyright © 2016 Sabina Tangaro et al. https://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research Article
Tangaro, Sabina
Fanizzi, Annarita
Amoroso, Nicola
Corciulo, Roberto
Garuccio, Elena
Gesualdo, Loreto
Loizzo, Giuliana
Procaccini, Deni Aldo
Vernò, Lucia
Bellotti, Roberto
Computer Aided Detection System for Prediction of the Malaise during Hemodialysis
title Computer Aided Detection System for Prediction of the Malaise during Hemodialysis
title_full Computer Aided Detection System for Prediction of the Malaise during Hemodialysis
title_fullStr Computer Aided Detection System for Prediction of the Malaise during Hemodialysis
title_full_unstemmed Computer Aided Detection System for Prediction of the Malaise during Hemodialysis
title_short Computer Aided Detection System for Prediction of the Malaise during Hemodialysis
title_sort computer aided detection system for prediction of the malaise during hemodialysis
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4799825/
https://www.ncbi.nlm.nih.gov/pubmed/27042200
http://dx.doi.org/10.1155/2016/8748156
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