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Cited by in CrossRef
For: Byeon H. Development of a depression in Parkinson's disease prediction model using machine learning. World J Psychiatr 2020; 10(10): 234-244 [PMID: 33134114 DOI: 10.5498/wjp.v10.i10.234]
URL: https://www.wjgnet.com/2220-3206/full/v10/i10/234.htm
Number Citing Articles
1
Haewon Byeon. Screening dementia and predicting high dementia risk groups using machine learningWorld Journal of Psychiatry 2022; 12(2): 204-211 doi: 10.5498/wjp.v12.i2.204
2
Kaixin Dou, Jiangnan Ma, Xue Zhang, Wanda Shi, Mingzhu Tao, Anmu Xie. Multi-predictor modeling for predicting early Parkinson’s disease and non-motor symptoms progressionFrontiers in Aging Neuroscience 2022; 14 doi: 10.3389/fnagi.2022.977985
3
Hessa Alfalahi, Sofia B. Dias, Ahsan H. Khandoker, Kallol Ray Chaudhuri, Leontios J. Hadjileontiadis. A scoping review of neurodegenerative manifestations in explainable digital phenotypingnpj Parkinson's Disease 2023; 9(1) doi: 10.1038/s41531-023-00494-0
4
Khalid Orayj, Tahani Almeleebia, Easwaran Vigneshwaran, Sultan Alshahrani, Sirajudeen. S. Alavudeen, Wael Alghamdi. Trend of recognizing depression symptoms and antidepressants use in newly diagnosed Parkinson's disease: Population‐based studyBrain and Behavior 2021; 11(8) doi: 10.1002/brb3.2228
5
Baile Ning, Zhifang Wang, Qian Wu, Qiyue Deng, Qing Yang, Jing Gao, Wen Fu, Ying Deng, Bingxin Wu, Xichang Huang, Jilin Mei, Wenbin Fu. Acupuncture inhibits autophagy and repairs synapses by activating the mTOR pathway in Parkinson’s disease depression model ratsBrain Research 2023; 1808: 148320 doi: 10.1016/j.brainres.2023.148320
6
Saraswati Patil, Sangita Jaybhaye, Sujal Bokariya, Pranav Jain, Siddhi Phapale, Tejas Hande, A.C. Sumathi, N. Yuvaraj, N.H. Ghazali. Parkinson’s Disease Prediction System in Machine LearningITM Web of Conferences 2023; 56: 05002 doi: 10.1051/itmconf/20235605002
7
Zhifang Wang, Menglin Kou, Qiyue Deng, Haotian Yu, Jilin Mei, Jing Gao, Wen Fu, Baile Ning. Acupuncture activates IRE1/XBP1 endoplasmic reticulum stress pathway in Parkinson's disease model ratsBehavioural Brain Research 2024; 462: 114871 doi: 10.1016/j.bbr.2024.114871
8
Min Seong Kim, Hyesoo Kim, Gabsang Lee. Precision Medicine in Parkinson's Disease Using Induced Pluripotent Stem CellsAdvanced Healthcare Materials 2024;  doi: 10.1002/adhm.202303041
9
Arivarasi A., Alagiri Govindasamy, Sathiya Narayanan S.. Principles and Applications of Socio-Cognitive and Affective ComputingAdvances in Computational Intelligence and Robotics 2022; : 130 doi: 10.4018/978-1-6684-3843-5.ch009
10
Juntao Tan, Zhengguo Xu, Yuxin He, Lingqin Zhang, Shoushu Xiang, Qian Xu, Xiaomei Xu, Jun Gong, Chao Tan, Langmin Tan. A web-based novel prediction model for predicting depression in elderly patients with coronary heart disease: A multicenter retrospective, propensity-score matched studyFrontiers in Psychiatry 2022; 13 doi: 10.3389/fpsyt.2022.949753
11
Yumeng Yan, Yiqian Du, Xue Li, Weiwei Ping, Yunqi Chang. Physical function, ADL, and depressive symptoms in Chinese elderly: Evidence from the CHARLSFrontiers in Public Health 2023; 11 doi: 10.3389/fpubh.2023.1017689
12
Rakesh Kumar, Meenu Gupta, Simarpreet Singh. Early Prediction of Parkinson’s Disease using Multiple SVM Classifiers2023 International Conference on Sustainable Computing and Smart Systems (ICSCSS) 2023; : 37 doi: 10.1109/ICSCSS57650.2023.10169162
13
Hung Viet Nguyen, Haewon Byeon. Prediction of Parkinson’s Disease Depression Using LIME-Based Stacking Ensemble ModelMathematics 2023; 11(3): 708 doi: 10.3390/math11030708
14
Md Belal Bin Heyat, Faijan Akhtar, Farwa Munir, Arshiya Sultana, Abdullah Y. Muaad, Ijaz Gul, Mohamad Sawan, Waseem Asghar, Sheikh Muhammad Asher Iqbal, Atif Amin Baig, Isabel de la Torre Díez, Kaishun Wu. Unravelling the complexities of depression with medical intelligence: exploring the interplay of genetics, hormones, and brain functionComplex & Intelligent Systems 2024;  doi: 10.1007/s40747-024-01346-x
15
Haewon Byeon. Developing a nomogram for predicting the depression of senior citizens living alone while focusing on perceived social supportWorld Journal of Psychiatry 2021; 11(12): 1314-1327 doi: 10.5498/wjp.v11.i12.1314
16
Sweta Bhadra, Chandan Jyoti Kumar. An insight into diagnosis of depression using machine learning techniques: a systematic reviewCurrent Medical Research and Opinion 2022; 38(5): 749 doi: 10.1080/03007995.2022.2038487
17
Haewon Byeon. Predicting the Severity of Parkinson’s Disease Dementia by Assessing the Neuropsychiatric Symptoms with an SVM Regression ModelInternational Journal of Environmental Research and Public Health 2021; 18(5): 2551 doi: 10.3390/ijerph18052551
18
Naresh Alapati, N. Anusha, P Joharika, N. Jenny Jerusha, P Tanuja. Prediction of Parkinson's Disease using Machine Learning2023 Second International Conference on Electronics and Renewable Systems (ICEARS) 2023; : 1357 doi: 10.1109/ICEARS56392.2023.10085443