Clinical assessment of olfactory dysfunction in Parkinson's disease

1998 ◽  
Vol 13 (3) ◽  
pp. 394-399 ◽  
Author(s):  
Constantin Potagas ◽  
George Dellatolas ◽  
Marc Ziegler ◽  
Jean Leveteau ◽  
Nguyen Bathien ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Clinical Assessment ◽  
Olfactory Dysfunction

Clinical assessment of olfactory dysfunction in Parkinson's disease

1999 ◽  
Vol 14 (2) ◽  
pp. 385-386
Author(s):  
M. R. Mansoor ◽  
D. R. Prichard ◽  
J. P. Hobson ◽  
R. J. Meara
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Clinical Assessment ◽  
Olfactory Dysfunction

scholarly journals ASSESSMENT OF OLFACTORY DYSFUNCTION IN PARKINSON’S DISEASE PATIENTS

2017 ◽  
Vol 4 (7) ◽  
pp. 528-536
Author(s):  
Akhilesh Kumar Singh ◽  
◽  
Bal Krishana ◽  
Meena Gupta ◽  
◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Olfactory Dysfunction

scholarly journals Comparative Motor Pre-clinical Assessment in Parkinson’s Disease Using Supervised Machine Learning Approaches

2018 ◽  
Vol 46 (12) ◽  
pp. 2057-2068 ◽  
Author(s):  
Erika Rovini ◽  
Carlo Maremmani ◽  
Alessandra Moschetti ◽  
Dario Esposito ◽  
Filippo Cavallo
Keyword(s):  
Machine Learning ◽  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Clinical Assessment ◽  
Supervised Machine Learning ◽  
Learning Approaches

Olfactory Dysfunction and Dementia in Parkinson's Disease

2014 ◽  
Vol 4 (2) ◽  
pp. 181-187 ◽  
Author(s):  
Atsushi Takeda ◽  
Toru Baba ◽  
Akio Kikuchi ◽  
Takafumi Hasegawa ◽  
Naoto Sugeno ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Olfactory Dysfunction

Feature olfactory dysfunction and gut microbiota in Parkinson's disease

2017 ◽  
Vol 381 ◽  
pp. 1055
Author(s):  
O. Izhboldina ◽  
I. Zhukova ◽  
N. Zhukova ◽  
V. Alifirova ◽  
A. Latypova ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Gut Microbiota ◽  
Olfactory Dysfunction

scholarly journals PhoneMD: Learning to Diagnose Parkinson’s Disease from Smartphone Data

2019 ◽  
Vol 33 ◽  
pp. 1118-1125 ◽  
Author(s):  
Patrick Schwab ◽  
Walter Karlen
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Clinical Assessment ◽  
Operating Characteristic ◽  
Characteristic Curve ◽  
Predictive Performance ◽  
Learning Approach ◽  
Machine Learning Approach ◽  
Term Data

Parkinson’s disease is a neurodegenerative disease that can affect a person’s movement, speech, dexterity, and cognition. Clinicians primarily diagnose Parkinson’s disease by performing a clinical assessment of symptoms. However, misdiagnoses are common. One factor that contributes to misdiagnoses is that the symptoms of Parkinson’s disease may not be prominent at the time the clinical assessment is performed. Here, we present a machine-learning approach towards distinguishing between people with and without Parkinson’s disease using long-term data from smartphone-based walking, voice, tapping and memory tests. We demonstrate that our attentive deep-learning models achieve significant improvements in predictive performance over strong baselines (area under the receiver operating characteristic curve = 0.85) in data from a cohort of 1853 participants. We also show that our models identify meaningful features in the input data. Our results confirm that smartphone data collected over extended periods of time could in the future potentially be used as a digital biomarker for the diagnosis of Parkinson’s disease.

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