Using unsupervised machine learning to classify behavioral risk markers of bacterial vaginosis

被引:0
|
作者
Violeta J. Rodriguez
Yue Pan
Ana S. Salazar
Nicholas Fonseca Nogueira
Patricia Raccamarich
Nichole R. Klatt
Deborah L. Jones
Maria L. Alcaide
机构
[1] University of Miami Miller School of Medicine,Department of Psychiatry and Behavioral Sciences
[2] University of Georgia,Department of Psychology
[3] University of Miami Miller School of Medicine,Division of Biostatistics, Department of Public Health Sciences
[4] University of Miami Miller School of Medicine,Division of Infectious Diseases, Department of Medicine
[5] University of Minnesota,Surgical Outcomes and Precision Medicine Research Division, Department of Surgery
来源
Archives of Gynecology and Obstetrics | 2024年 / 309卷
关键词
Bacterial vaginosis; Unsupervised machine learning; Sexual behavior; Women;
D O I
暂无
中图分类号
学科分类号
摘要
引用
收藏
页码:1053 / 1063
页数:10
相关论文
共 50 条
  • [41] Unsupervised machine learning for disease prediction: a comparative performance analysis using multiple datasets
    Haohui Lu
    Shahadat Uddin
    Health and Technology, 2024, 14 : 141 - 154
  • [42] Automatic Detection of Subsurface Defects in Composite Materials using Thermography and Unsupervised Machine Learning
    Marani, Roberto
    Palumbo, Davide
    Galietti, Umberto
    Stella, Ettore
    D'Orazio, Tiziana
    2016 IEEE 8TH INTERNATIONAL CONFERENCE ON INTELLIGENT SYSTEMS (IS), 2016, : 516 - 521
  • [43] Identifying Users' Concerns in Lodging Sharing Economy Using Unsupervised Machine Learning Approach
    Al-Ramahi, Mohammad
    Ahmed, Ali
    2019 2ND INTERNATIONAL CONFERENCE ON DATA INTELLIGENCE AND SECURITY (ICDIS 2019), 2019, : 160 - 166
  • [44] Ranking the Importance of Variables in a Nonparametric Frontier Analysis Using Unsupervised Machine Learning Techniques
    Moragues, Raul
    Aparicio, Juan
    Esteve, Miriam
    MATHEMATICS, 2023, 11 (11)
  • [45] Classification and Identification of Spectral Pixels with Low Maritime Occupancy Using Unsupervised Machine Learning
    Seo, Dongmin
    Oh, Sangwoo
    Lee, Daekyeom
    REMOTE SENSING, 2022, 14 (08)
  • [46] Unsupervised machine learning for disease prediction: a comparative performance analysis using multiple datasets
    Lu, Haohui
    Uddin, Shahadat
    HEALTH AND TECHNOLOGY, 2024, 14 (01) : 141 - 154
  • [47] Analyzing continuous infrasound from Stromboli volcano, Italy using unsupervised machine learning
    Witsil, Alex J. C.
    Johnson, Jeffrey B.
    COMPUTERS & GEOSCIENCES, 2020, 140
  • [48] Unsupervised machine learning using K-means identifies radiomic subgroups of pediatric low-grade gliomas that correlate with key molecular markers
    Haldar, Debanjan
    Kazerooni, Anahita Fathi
    Arif, Sherjeel
    Familiar, Ariana
    Madhogarhia, Rachel
    Khalili, Nastaran
    Bagheri, Sina
    Anderson, Hannah
    Shaikh, Ibraheem Salman
    Mahtabfar, Aria
    Kim, Meen Chul
    Tu, Wenxin
    Ware, Jefferey
    Vossough, Arastoo
    Davatzikos, Christos
    Storm, Phillip B.
    Resnick, Adam
    Nabavizadeh, Ali
    NEOPLASIA, 2023, 36
  • [49] Social Media Markers to Identify Fathers at Risk of Postpartum Depression: A Machine Learning Approach
    Shatte, Adrian B. R.
    Hutchinson, Delyse M.
    Fuller-Tyszkiewicz, Matthew
    Teague, Samantha J.
    CYBERPSYCHOLOGY BEHAVIOR AND SOCIAL NETWORKING, 2020, 23 (09) : 611 - 618
  • [50] Unsupervised Machine Learning for Lithological Mapping Using Geochemical Data in Covered Areas of Jining, China
    Guopeng Wu
    Guoxiong Chen
    Qiuming Cheng
    Zhenjie Zhang
    Jie Yang
    Natural Resources Research, 2021, 30 : 1053 - 1068