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Articles Published Processes
10/21/2020 6:25:15 AM | Browse: 565 | Download: 1874
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Received |
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2020-03-29 14:58 |
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Peer-Review Started |
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2020-03-30 09:57 |
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To Make the First Decision |
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Return for Revision |
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2020-08-22 21:32 |
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Revised |
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2020-09-01 01:56 |
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Second Decision |
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2020-09-22 10:46 |
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Accepted by Journal Editor-in-Chief |
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Accepted by Executive Editor-in-Chief |
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2020-09-22 20:24 |
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Articles in Press |
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2020-09-22 20:24 |
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Publication Fee Transferred |
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Edit the Manuscript by Language Editor |
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2020-10-10 06:32 |
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Typeset the Manuscript |
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2020-10-19 01:05 |
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Publish the Manuscript Online |
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2020-10-21 06:25 |
| ISSN |
2220-3206 (online) |
| Open Access |
This article is an open-access article that was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution NonCommercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: http://creativecommons.org/Licenses/by-nc/4.0/ |
| Copyright |
© The Author(s) 2020. Published by Baishideng Publishing Group Inc. All rights reserved. |
| Article Reprints |
For details, please visit: http://www.wjgnet.com/bpg/gerinfo/247
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| Permissions |
For details, please visit: http://www.wjgnet.com/bpg/gerinfo/207
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| Publisher |
Baishideng Publishing Group Inc, 7041 Koll Center Parkway, Suite 160, Pleasanton, CA 94566, USA |
| Website |
http://www.wjgnet.com |
| Category |
Psychiatry |
| Manuscript Type |
Observational Study |
| Article Title |
Development of a depression in Parkinson's disease prediction model using machine learning
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| Manuscript Source |
Invited Manuscript |
| All Author List |
Haewon Byeon |
| ORCID |
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| Funding Agency and Grant Number |
| Funding Agency |
Grant Number |
| National Research Foundation of Korea |
NRF-2019S1A5A8034211 |
| National Research Foundation of Korea |
NRF-2018R1D1A1B07041091 |
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| Corresponding Author |
Haewon Byeon, DSc, PhD, Professor, Major in Medical Big Data, College of AI Convergence, Inje University, Major in Medical Big Data, College of AI Convergence, Inje University, Gimhae 50834, Gyeonsangnamdo, South Korea. bhwpuma@naver.com |
| Key Words |
Depression in Parkinson's disease; Supervised Machine Learning; Neuropsychological test; Risk factor; Support Vector Machine; Rapid eye movement sleep behavior disorders |
| Core Tip |
When the effects of parkinson’s disease (PD) motor symptoms were compared using “functional weight”, occurrence of levodopa-induced dyskinesia were the most influential risk factor of diagnose the Parkinson’s disease (DPD). The results can be used as baseline information to prevent DPD and establish management strategies. It is necessary to develop customized screening tests that can detect the DPD patients in the early stage and monitor high-risk groups continuously based on the factors related to DPD derived from this predictive model in order to maintain the emotional health of PD. It is also necessary to develop customized programs for managing depression from the onset of PD. |
| Publish Date |
2020-10-21 06:25 |
| Citation |
Byeon H. Development of a depression in Parkinson's disease prediction model using machine learning. World J Psychiatr 2020; 10(10): 234-244 |
| URL |
https://www.wjgnet.com/2220-3206/full/v10/i10/234.htm |
| DOI |
https://dx.doi.org/10.5498/wjp.v10.i10.234 |
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