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PD_Manager: an mHealth platform for Parkinson's disease patient management
PD_Manager is a mobile health platform designed to cover most of the aspects regarding the management of Parkinson's disease (PD) in a holistic approach. Patients are unobtrusively monitored using commercial wrist and insole sensors paired with a smartphone, to automatically estimate the severi...
Autores principales: | , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
The Institution of Engineering and Technology
2017
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5496467/ https://www.ncbi.nlm.nih.gov/pubmed/28706727 http://dx.doi.org/10.1049/htl.2017.0007 |
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author | Tsiouris, Kostas M. Gatsios, Dimitrios Rigas, George Miljkovic, Dragana Koroušić Seljak, Barbara Bohanec, Marko Arredondo, Maria T. Antonini, Angelo Konitsiotis, Spyros Koutsouris, Dimitrios D. Fotiadis, Dimitrios I. |
author_facet | Tsiouris, Kostas M. Gatsios, Dimitrios Rigas, George Miljkovic, Dragana Koroušić Seljak, Barbara Bohanec, Marko Arredondo, Maria T. Antonini, Angelo Konitsiotis, Spyros Koutsouris, Dimitrios D. Fotiadis, Dimitrios I. |
author_sort | Tsiouris, Kostas M. |
collection | PubMed |
description | PD_Manager is a mobile health platform designed to cover most of the aspects regarding the management of Parkinson's disease (PD) in a holistic approach. Patients are unobtrusively monitored using commercial wrist and insole sensors paired with a smartphone, to automatically estimate the severity of most of the PD motor symptoms. Besides motor symptoms monitoring, the patient's mobile application also provides various non-motor self-evaluation tests for assessing cognition, mood and nutrition to motivate them in becoming more active in managing their disease. All data from the mobile application and the sensors is transferred to a cloud infrastructure to allow easy access for clinicians and further processing. Clinicians can access this information using a separate mobile application that is specifically designed for their respective needs to provide faster and more accurate assessment of PD symptoms that facilitate patient evaluation. Machine learning techniques are used to estimate symptoms and disease progression trends to further enhance the provided information. The platform is also complemented with a decision support system (DSS) that notifies clinicians for the detection of new symptoms or the worsening of existing ones. As patient's symptoms are progressing, the DSS can also provide specific suggestions regarding appropriate medication changes. |
format | Online Article Text |
id | pubmed-5496467 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2017 |
publisher | The Institution of Engineering and Technology |
record_format | MEDLINE/PubMed |
spelling | pubmed-54964672017-07-13 PD_Manager: an mHealth platform for Parkinson's disease patient management Tsiouris, Kostas M. Gatsios, Dimitrios Rigas, George Miljkovic, Dragana Koroušić Seljak, Barbara Bohanec, Marko Arredondo, Maria T. Antonini, Angelo Konitsiotis, Spyros Koutsouris, Dimitrios D. Fotiadis, Dimitrios I. Healthc Technol Lett Special Issue: Addressing Age-related Conditions: technologies for early detection PD_Manager is a mobile health platform designed to cover most of the aspects regarding the management of Parkinson's disease (PD) in a holistic approach. Patients are unobtrusively monitored using commercial wrist and insole sensors paired with a smartphone, to automatically estimate the severity of most of the PD motor symptoms. Besides motor symptoms monitoring, the patient's mobile application also provides various non-motor self-evaluation tests for assessing cognition, mood and nutrition to motivate them in becoming more active in managing their disease. All data from the mobile application and the sensors is transferred to a cloud infrastructure to allow easy access for clinicians and further processing. Clinicians can access this information using a separate mobile application that is specifically designed for their respective needs to provide faster and more accurate assessment of PD symptoms that facilitate patient evaluation. Machine learning techniques are used to estimate symptoms and disease progression trends to further enhance the provided information. The platform is also complemented with a decision support system (DSS) that notifies clinicians for the detection of new symptoms or the worsening of existing ones. As patient's symptoms are progressing, the DSS can also provide specific suggestions regarding appropriate medication changes. The Institution of Engineering and Technology 2017-05-23 /pmc/articles/PMC5496467/ /pubmed/28706727 http://dx.doi.org/10.1049/htl.2017.0007 Text en http://creativecommons.org/licenses/by/3.0/ This is an open access article published by the IET under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0/) |
spellingShingle | Special Issue: Addressing Age-related Conditions: technologies for early detection Tsiouris, Kostas M. Gatsios, Dimitrios Rigas, George Miljkovic, Dragana Koroušić Seljak, Barbara Bohanec, Marko Arredondo, Maria T. Antonini, Angelo Konitsiotis, Spyros Koutsouris, Dimitrios D. Fotiadis, Dimitrios I. PD_Manager: an mHealth platform for Parkinson's disease patient management |
title | PD_Manager: an mHealth platform for Parkinson's disease patient management |
title_full | PD_Manager: an mHealth platform for Parkinson's disease patient management |
title_fullStr | PD_Manager: an mHealth platform for Parkinson's disease patient management |
title_full_unstemmed | PD_Manager: an mHealth platform for Parkinson's disease patient management |
title_short | PD_Manager: an mHealth platform for Parkinson's disease patient management |
title_sort | pd_manager: an mhealth platform for parkinson's disease patient management |
topic | Special Issue: Addressing Age-related Conditions: technologies for early detection |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5496467/ https://www.ncbi.nlm.nih.gov/pubmed/28706727 http://dx.doi.org/10.1049/htl.2017.0007 |
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