Machine Learning Informed Diagnosis for Congenital Heart Disease in Large Claims Data Source.

Journal: JACC. Advances
Published Date: Dec 25, 2023

Abstract

BACKGROUND: With an increasing interest in using large claims databases in medical practice and research, it is a meaningful and essential step to efficiently identify patients with the disease of interest.

Authors

  • Ariane J Marelli
    McGill University Health Centre, McGill Adult Unit for Congenital Heart Disease Excellence, Montreal, Québec, Canada.
  • Chao Li
    McGill University Health Centre, McGill Adult Unit for Congenital Heart Disease Excellence, Montreal, Québec, Canada.
  • Aihua Liu
    McGill University Health Centre, McGill Adult Unit for Congenital Heart Disease Excellence, Montreal, Québec, Canada.
  • Hanh Nguyen
    McGill University Health Centre, McGill Adult Unit for Congenital Heart Disease Excellence, Montreal, Québec, Canada.
  • Harry Moroz
    McGill University Health Centre, McGill Adult Unit for Congenital Heart Disease Excellence, Montreal, Québec, Canada.
  • James M Brophy
    Department of Epidemiology, Biostatistics, and Occupational Health, McGill University, Montreal, Québec, Canada.
  • Liming Guo
    McGill University Health Centre, McGill Adult Unit for Congenital Heart Disease Excellence, Montreal, Québec, Canada.
  • David L Buckeridge
    Department of Epidemiology, Biostatistics, and Occupational Health, McGill University, Montreal, Québec, Canada.
  • Jian Tang
    Department of Decision Sciences HEC, Université de Montréal, Montreal, Québec, Canada.
  • Archer Y Yang
    Department of Mathematics and Statistics, McGill University, Montreal, Québec, Canada.
  • Yue Li
    School of Computer Science and Software Engineering, University of Science and Technology Liaoning, Anshan, China.

Keywords

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