Decision Making in Health Care Diagnosis: Evidence From Parkinson's Disease Via Hybrid Machine Learning

2021 ◽  
pp. 1-13
Author(s):  
Jinil Persis Devarajan ◽  
V. Raja Sreedharan ◽  
Gopalakrishnan Narayanamurthy
Keyword(s):  
Machine Learning ◽  
Parkinson’S Disease ◽  
Health Care ◽  
Decision Making ◽  
Parkinson's Disease ◽  
Hybrid Machine

scholarly journals Diagnosis of Parkinson’s Disorder through Speech Data using Machine Learning Algorithms

2020 ◽  
Vol 9 (3) ◽  
pp. 69-72
Keyword(s):  
Machine Learning ◽  
Parkinson’S Disease ◽  
Health Care ◽  
Decision Making ◽  
Parkinson's Disease ◽  
Neurodegenerative Disorder ◽  
Machine Learning Algorithms ◽  
Speech Data ◽  
Computer Aided ◽  
Comprehensive Comparison

Parkinson's disease is a neurodegenerative disorder that affects millions of people around the globe. Detecting Parkinson's disease at an earlier stage could help to better diagnose the disease. Machine learning provides potentially large opportunities for computer-aided identification and diagnosis that could minimize unavoidable health care errors and inherent clinical uncertainty, provide guidance, and improve decision-making. In this paper, we explore the feature extraction and prediction algorithms used to predict Parkinson's disease and provide a comprehensive comparison of these algorithms

scholarly journals Longitudinal clustering analysis and prediction of Parkinson’s disease progression using radiomics and hybrid machine learning

2021 ◽  
Vol 0 (0) ◽  
pp. 0-0
Author(s):  
Mohammad R. Salmanpour ◽  
Mojtaba Shamsaei ◽  
Ghasem Hajianfar ◽  
Hamid Soltanian-Zadeh ◽  
Arman Rahmim
Keyword(s):  
Machine Learning ◽  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Disease Progression ◽  
Clustering Analysis ◽  
Hybrid Machine

Robust identification of Parkinson's disease subtypes using radiomics and hybrid machine learning

2021 ◽  
Vol 129 ◽  
pp. 104142
Author(s):  
Mohammad R. Salmanpour ◽  
Mojtaba Shamsaei ◽  
Abdollah Saberi ◽  
Ghasem Hajianfar ◽  
Hamid Soltanian-Zadeh ◽  
...  
Keyword(s):  
Machine Learning ◽  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Robust Identification ◽  
Disease Subtypes ◽  
Hybrid Machine

Dyadic Decision-Making in Advanced Parkinson’s Disease: A Mixed Methods Study

2019 ◽  
Vol 42 (5) ◽  
pp. 348-355 ◽  
Author(s):  
Barbara Habermann ◽  
Ju Young Shin ◽  
Gretchen Shearer
Keyword(s):  
Parkinson’S Disease ◽  
Health Care ◽  
Decision Making ◽  
Parkinson's Disease ◽  
Mixed Methods ◽  
Health Care Providers ◽  
Assisted Living ◽  
Decision Quality ◽  
Care Providers ◽  
Advanced Parkinson’S Disease

People with advanced Parkinson’s disease (PD) are living at home being cared for by a family member. Decisions about health care and living preferences are made in a family context. The aims of the study were to (a) examine the types and timing of the decisions being made by dyads (person with Parkinson’s [PWP] and caregiver) in advanced PD; and (b) explore perceived decision quality relative to specific decisions made. A mixed methods design of semi-structured dyad interviews followed by individual completion of decision measures twice at six months apart was utilized. Decisions involved obtaining more services in the home, moving into assisted living communities, maintaining as is, and initiating hospice. There was high decision quality as reflected by low decisional conflict and regret without statistical differences within the dyad. The findings provide insight into the nature of decisions dyads face and suggest ways that health care providers can support decision-making.

Multiclass machine learning classification of functional brain images for Parkinson's disease stage prediction

2020 ◽  
Vol 13 (5) ◽  
pp. 508-523 ◽  
Author(s):  
Guan‐Hua Huang ◽  
Chih‐Hsuan Lin ◽  
Yu‐Ren Cai ◽  
Tai‐Been Chen ◽  
Shih‐Yen Hsu ◽  
...  
Keyword(s):  
Machine Learning ◽  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Disease Stage ◽  
Brain Images ◽  
Functional Brain ◽  
Machine Learning Classification
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