Detecting cardiovascular diseases using unsupervised machine learning clustering based on electronic medical records.

Journal: BMC medical research methodology
Published Date: Dec 19, 2024

Abstract

BACKGROUND: Electronic medical records (EMR)-trained machine learning models have the potential in CVD risk prediction by integrating a range of medical data from patients, facilitate timely diagnosis and classification of CVDs. We tested the hypothesis that unsupervised ML approach utilizing EMR could be used to develop a new model for detecting prevalent CVD in clinical settings.

Authors

  • Ying Hu
    Department of Ultrasonography, The First Affiliated Hospital, College of Medicine, Zhejiang University, Qingchun Road No. 79, Hangzhou, Zhejiang 310003, China.
  • Hai Yan
    School of Chemistry and Biological Engineering, University of Science and Technology Beijing, Beijing, China.
  • Ming Liu
    School of Land Engineering, Chang'an University, Xi'an 710064, China; Xi'an Key Laboratory of Territorial Spatial Information, School of Land Engineering, Chang'an University, Xi'an 710064, China. Electronic address: mingliu@chd.edu.cn.
  • Jing Gao
    Department of Gastroenterology 3, Hubei University of Medicine, Renmin Hospital, Shiyan, Hubei, China.
  • Lianhong Xie
    Shanghai Xuhui Central Hospital, Zhongshan-Xuhui Hospital, Fudan University, Shanghai, 200031, China.
  • Chunyu Zhang
    Department of Neurosurgery, Shanghai East Hospital, School of Medicine, Tongji University, Shanghai, China.
  • Lili Wei
    Shandong Institute for Food and Drug Control, Ji'nan 250101, China.
  • Yinging Ding
    Department of Epidemiology, School of Public Health, and Key Laboratory of Public Health Safety of Ministry of Education, Fudan University, Shanghai, 200032, China. dingyy@fudan.edu.cn.
  • Hong Jiang
    Department of Neurosurgery, Rui-Jin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.

Keywords

External Resources

Popular Topics
Recent Journals