Determining In-Depth Information of Mild Cognitive Impairment: Clustering Using Unsupervised Machine Learning

被引：0
|
作者：
Hiransuthikul, Akarin [1 ,2 ]
Chunharas, Chaipat [1 ,2 ]
Thongkam, Achitpol [3 ]
Muadmanee, Wisawin [3 ]
Chotibut, Thiparat [4 ]
Phusuwan, Waragon [2 ]
Petchlorlian, Aisawan
Praditpornsilpa, Kearkiat [5 ]
机构：
[1] Chulalongkorn Univ, Fac Med, Div Neurol, Dept Med, Bangkok, Thailand
[2] Chulalongkorn Univ, Chulalongkorn Cognit Clin & Computat Neurosci, Dept Med, Bangkok, Thailand
[3] Chulalongkorn Univ, Fac Med, Dept Med, Bangkok, Thailand
[4] Chulalongkorn Univ, Fac Med, Dept Med, Div Geriatr Med, Bangkok, Thailand
[5] Chulalongkorn Univ, Dept Med, Fac Med, Div Nephrol, Bangkok, Thailand
来源：
NEUROLOGY | 2021年 / 96卷 / 15期
关键词：
D O I：
暂无
中图分类号：
R74 [神经病学与精神病学];
学科分类号：
摘要：
1352
引用
收藏
页数：3
相关论文
共 50 条
  • [11] Clustering Seismocardiographic Events using Unsupervised Machine Learning
    Gamage, Peshala T.
    Azad, Md Khurshidul.
    Taebi, Amirtaha
    Sandler, Richard H.
    Mansy, Hansen A.
    2018 IEEE SIGNAL PROCESSING IN MEDICINE AND BIOLOGY SYMPOSIUM (SPMB), 2018,
  • [12] A Machine Learning Framework for Predicting Dementia and Mild Cognitive Impairment
    Stamate, Daniel
    Alghamdi, Wajdi
    Ogg, Jeremy
    Hoile, Richard
    Murtagh, Fionn
    2018 17TH IEEE INTERNATIONAL CONFERENCE ON MACHINE LEARNING AND APPLICATIONS (ICMLA), 2018, : 671 - 678
  • [13] Detection of mild cognitive impairment using various types of gait tests and machine learning
    Seifallahi, Mahmoud
    Galvin, James E.
    Ghoraani, Behnaz
    FRONTIERS IN NEUROLOGY, 2024, 15
  • [14] Unsupervised learning for hierarchical clustering using statistical information
    Okamoto, M
    Bu, N
    Tsuji, T
    ADVANCES IN NEURAL NETWORKS - ISNN 2004, PT 1, 2004, 3173 : 834 - 839
  • [15] In-depth analysis of reaction kinetics parameters of phenolic resin using molecular dynamics and unsupervised machine learning approach
    Bhesania, Abhishek S.
    Kamboj, Parvesh
    Peddakotla, Sai Abhishek
    Kumar, Rakesh
    COMPUTATIONAL MATERIALS SCIENCE, 2022, 206
  • [16] Robust Discovery of Mild Cognitive Impairment Subtypes and Their Risk of Alzheimer's Disease Conversion Using Unsupervised Machine Learning and Gaussian Mixture Modeling
    Nezhadmoghadam, Fahimeh
    Martinez-Torteya, Antonio
    Trevino, Victor
    Martinez, Emmanuel
    Santos, Alejandro
    Tamez-Pena, Jose
    CURRENT ALZHEIMER RESEARCH, 2021, 18 (07) : 595 - 606
  • [17] Highway Project Clustering Using Unsupervised Machine Learning Approach
    Alikhani, Hamed
    Jeong, H. David
    COMPUTING IN CIVIL ENGINEERING 2021, 2022, : 172 - 179
  • [18] An Approach for Clustering of Seismic Events using Unsupervised Machine Learning
    Karmenova, Markhaba
    Tlebaldinova, Aizhan
    Krak, Iurii
    Denissova, Natalya
    Popova, Galina
    Zhantassova, Zheniskul
    Ponkina, Elena
    Gyorok, Gyorgy
    ACTA POLYTECHNICA HUNGARICA, 2022, 19 (05) : 7 - 22
  • [19] Machine learning based on functional and structural connectivity in mild cognitive impairment
    Li, Yan
    Shao, Yongjia
    Wang, Junlang
    Liu, Yu
    Yang, Yuhan
    Wang, Zijian
    Xi, Qian
    MAGNETIC RESONANCE IMAGING, 2024, 109 : 10 - 17
  • [20] Utility of MemTrax and Machine Learning Modeling in Classification of Mild Cognitive Impairment
    Bergeron, Michael F.
    Landset, Sara
    Zhou, Xianbo
    Ding, Tao
    Khoshgoftaar, Taghi M.
    Zhao, Feng
    Du, Bo
    Chen, Xinjie
    Wang, Xuan
    Zhong, Lianmei
    Liu, Xiaolei
    Ashford, J. Wesson
    JOURNAL OF ALZHEIMERS DISEASE, 2020, 77 (04) : 1545 - 1558
12345