Predicting rapid progression of Parkinson's Disease at baseline patients evaluation

Kostas M. Tsiouris; Georgios Rigas; Dimitrios Gatsios; Angelo Antonini; Spiros Konitsiotis; Dimitrios D. Koutsouris; Dimitrios I. Fotiadis

doi:10.1109/EMBC.2017.8037708

Predicting rapid progression of Parkinson's Disease at baseline patients evaluation

Kostas M. Tsiouris, Georgios Rigas, Dimitrios Gatsios, Angelo Antonini, Spiros Konitsiotis, Dimitrios D. Koutsouris, Dimitrios I. Fotiadis

Istituto di Neuroriabilitazione Motoria San Camillo

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

The rate of Parkinson's Disease (PD) progression in the initial post-diagnosis years can vary significantly. In this work, a methodology for the extraction of the most informative features for predicting rapid progression of the disease is proposed, using public data from the Parkinson's Progression Markers Initiative (PPMI) and machine learning techniques. The aim is to determine if a patient is at risk of expressing rapid progression of PD symptoms from the baseline evaluation and as close to diagnosis as possible. By examining the records of 409 patients from the PPMI dataset, the features with the best predictive value at baseline patient evaluation are found to be sleep problems, daytime sleepiness and fatigue, motor symptoms at legs, cognition impairment, early axial and facial symptoms and in the most rapidly advanced cases speech issues, loss of smell and affected leg muscle reflexes.

Original language	English
Title of host publication	2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society
Subtitle of host publication	Smarter Technology for a Healthier World, EMBC 2017 - Proceedings
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	3898-3901
Number of pages	4
ISBN (Electronic)	9781509028092
DOIs	https://doi.org/10.1109/EMBC.2017.8037708
Publication status	Published - Sep 13 2017
Event	39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2017 - Jeju Island, Korea, Republic of Duration: Jul 11 2017 → Jul 15 2017

Conference

Conference	39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2017
Country	Korea, Republic of
City	Jeju Island
Period	7/11/17 → 7/15/17

ASJC Scopus subject areas

Signal Processing
Biomedical Engineering
Computer Vision and Pattern Recognition
Health Informatics

Access to Document

10.1109/EMBC.2017.8037708

Fingerprint Dive into the research topics of 'Predicting rapid progression of Parkinson's Disease at baseline patients evaluation'. Together they form a unique fingerprint.

Cite this

Tsiouris, K. M., Rigas, G., Gatsios, D., Antonini, A., Konitsiotis, S., Koutsouris, D. D., & Fotiadis, D. I. (2017). Predicting rapid progression of Parkinson's Disease at baseline patients evaluation. In 2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society: Smarter Technology for a Healthier World, EMBC 2017 - Proceedings (pp. 3898-3901). [8037708] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/EMBC.2017.8037708

Predicting rapid progression of Parkinson's Disease at baseline patients evaluation. / Tsiouris, Kostas M.; Rigas, Georgios; Gatsios, Dimitrios; Antonini, Angelo; Konitsiotis, Spiros; Koutsouris, Dimitrios D.; Fotiadis, Dimitrios I.

2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society: Smarter Technology for a Healthier World, EMBC 2017 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2017. p. 3898-3901 8037708.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Tsiouris, KM, Rigas, G, Gatsios, D, Antonini, A, Konitsiotis, S, Koutsouris, DD & Fotiadis, DI 2017, Predicting rapid progression of Parkinson's Disease at baseline patients evaluation. in 2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society: Smarter Technology for a Healthier World, EMBC 2017 - Proceedings., 8037708, Institute of Electrical and Electronics Engineers Inc., pp. 3898-3901, 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2017, Jeju Island, Korea, Republic of, 7/11/17. https://doi.org/10.1109/EMBC.2017.8037708

Tsiouris KM, Rigas G, Gatsios D, Antonini A, Konitsiotis S, Koutsouris DD et al. Predicting rapid progression of Parkinson's Disease at baseline patients evaluation. In 2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society: Smarter Technology for a Healthier World, EMBC 2017 - Proceedings. Institute of Electrical and Electronics Engineers Inc. 2017. p. 3898-3901. 8037708 https://doi.org/10.1109/EMBC.2017.8037708

Tsiouris, Kostas M. ; Rigas, Georgios ; Gatsios, Dimitrios ; Antonini, Angelo ; Konitsiotis, Spiros ; Koutsouris, Dimitrios D. ; Fotiadis, Dimitrios I. / Predicting rapid progression of Parkinson's Disease at baseline patients evaluation. 2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society: Smarter Technology for a Healthier World, EMBC 2017 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2017. pp. 3898-3901

@inproceedings{bf28ca41ab2746e8bfb34a8f7a9d6faf,

title = "Predicting rapid progression of Parkinson's Disease at baseline patients evaluation",

abstract = "The rate of Parkinson's Disease (PD) progression in the initial post-diagnosis years can vary significantly. In this work, a methodology for the extraction of the most informative features for predicting rapid progression of the disease is proposed, using public data from the Parkinson's Progression Markers Initiative (PPMI) and machine learning techniques. The aim is to determine if a patient is at risk of expressing rapid progression of PD symptoms from the baseline evaluation and as close to diagnosis as possible. By examining the records of 409 patients from the PPMI dataset, the features with the best predictive value at baseline patient evaluation are found to be sleep problems, daytime sleepiness and fatigue, motor symptoms at legs, cognition impairment, early axial and facial symptoms and in the most rapidly advanced cases speech issues, loss of smell and affected leg muscle reflexes.",

author = "Tsiouris, {Kostas M.} and Georgios Rigas and Dimitrios Gatsios and Angelo Antonini and Spiros Konitsiotis and Koutsouris, {Dimitrios D.} and Fotiadis, {Dimitrios I.}",

year = "2017",

month = sep,

day = "13",

doi = "10.1109/EMBC.2017.8037708",

language = "English",

pages = "3898--3901",

booktitle = "2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

address = "United States",

note = "39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2017 ; Conference date: 11-07-2017 Through 15-07-2017",

}

TY - GEN

T1 - Predicting rapid progression of Parkinson's Disease at baseline patients evaluation

AU - Tsiouris, Kostas M.

AU - Rigas, Georgios

AU - Gatsios, Dimitrios

AU - Antonini, Angelo

AU - Konitsiotis, Spiros

AU - Koutsouris, Dimitrios D.

AU - Fotiadis, Dimitrios I.

PY - 2017/9/13

Y1 - 2017/9/13

N2 - The rate of Parkinson's Disease (PD) progression in the initial post-diagnosis years can vary significantly. In this work, a methodology for the extraction of the most informative features for predicting rapid progression of the disease is proposed, using public data from the Parkinson's Progression Markers Initiative (PPMI) and machine learning techniques. The aim is to determine if a patient is at risk of expressing rapid progression of PD symptoms from the baseline evaluation and as close to diagnosis as possible. By examining the records of 409 patients from the PPMI dataset, the features with the best predictive value at baseline patient evaluation are found to be sleep problems, daytime sleepiness and fatigue, motor symptoms at legs, cognition impairment, early axial and facial symptoms and in the most rapidly advanced cases speech issues, loss of smell and affected leg muscle reflexes.

AB - The rate of Parkinson's Disease (PD) progression in the initial post-diagnosis years can vary significantly. In this work, a methodology for the extraction of the most informative features for predicting rapid progression of the disease is proposed, using public data from the Parkinson's Progression Markers Initiative (PPMI) and machine learning techniques. The aim is to determine if a patient is at risk of expressing rapid progression of PD symptoms from the baseline evaluation and as close to diagnosis as possible. By examining the records of 409 patients from the PPMI dataset, the features with the best predictive value at baseline patient evaluation are found to be sleep problems, daytime sleepiness and fatigue, motor symptoms at legs, cognition impairment, early axial and facial symptoms and in the most rapidly advanced cases speech issues, loss of smell and affected leg muscle reflexes.

UR - http://www.scopus.com/inward/record.url?scp=85032181762&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85032181762&partnerID=8YFLogxK

U2 - 10.1109/EMBC.2017.8037708

DO - 10.1109/EMBC.2017.8037708

M3 - Conference contribution

AN - SCOPUS:85032181762

SP - 3898

EP - 3901

BT - 2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2017

Y2 - 11 July 2017 through 15 July 2017

ER -