Stratifying heart failure patients with graph neural network and transformer using Electronic Health Records to optimize drug response prediction.

Journal: Journal of the American Medical Informatics Association : JAMIA
PMID: 38926131

Abstract

OBJECTIVES: Heart failure (HF) impacts millions of patients worldwide, yet the variability in treatment responses remains a major challenge for healthcare professionals. The current treatment strategies, largely derived from population based evidence, often fail to consider the unique characteristics of individual patients, resulting in suboptimal outcomes. This study aims to develop computational models that are patient-specific in predicting treatment outcomes, by utilizing a large Electronic Health Records (EHR) database. The goal is to improve drug response predictions by identifying specific HF patient subgroups that are likely to benefit from existing HF medications.

Authors

  • Shaika Chowdhury
    Department of Artificial Intelligence and Informatics Research, Mayo Clinic, Rochester, MN 55902, United States.
  • Yongbin Chen
    Center for Brain Computer Interfaces and Brain Information Processing, South China University of Technology, Guangzhou, China.
  • Pengyang Li
    State Key Laboratory of Environmental Chemistry and Ecotoxicology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, China.
  • Sivaraman Rajaganapathy
    Department of Artificial Intelligence and Informatics Research, Mayo Clinic, Rochester, MN 55902, United States.
  • Andrew Wen
    Department of Medical Informatics and Clinical Epidemiology, Oregon Health & Science University, Portland, OR, USA.
  • Xiao Ma
    Department of Ultrasound, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China.
  • Qiying Dai
    Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN 55902, United States.
  • Yue Yu
    Department of Mathematics, Lehigh University, Bethlehem, PA, USA.
  • Sunyang Fu
    Department of Artificial Intelligence and Informatics, Mayo Clinic, Rochester, USA.
  • Xiaoqian Jiang
    School of Biomedical Informatics, University of Texas Health, Science Center at Houston, Houston, TX, USA.
  • Zhe He
    School of Information, Florida State University, Tallahassee, FL, USA.
  • Sunghwan Sohn
    Department of Artificial Intelligence and Informatics, Mayo Clinic, Rochester, USA.
  • Xiaoke Liu
    Department of Cardiovascular Medicine, Mayo Clinic, LaCrosse, WI, USA.
  • Suzette J Bielinski
    Department of Health Sciences Research, Mayo Clinic, Rochester, MN, 55905, USA. bielinski.suzette@mayo.edu.
  • Alanna M Chamberlain
    Department of Health Sciences Research, Mayo Clinic, Rochester, MN, United States.
  • James R Cerhan
    Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN 55902, United States.
  • Nansu Zong
    Health System Department of Biomedical Informatics, University of California, San Diego, La Jolla, California, USA.

Keywords

External Resources

Popular Topics
Recent Journals