Development of a Machine Learning-Powered Optimized Lung Allocation System for Maximum Benefits in Lung Transplantation: A Korean National Data.

Journal: Journal of Korean medical science
PMID: 39995255

Abstract

BACKGROUND: An ideal lung allocation system should reduce waiting list deaths, improve transplant survival, and ensure equitable organ allocation. This study aimed to develop a novel lung allocation score (LAS) system, the MaxBenefit LAS, to maximize transplant benefits.

Authors

  • Mihyang Ha
    Interdisplinary Program of Genomic Science, Pusan National University, Yangsan, Republic of Korea.
  • Woo Hyun Cho
    Division of Pulmonary, Allergy, and Critical Care Medicine, Department of Internal Medicine, Pusan National University Yangsan Hospital, Pusan National University School of Medicine, Yangsan, Korea.
  • Min Wook So
    Division of Rheumatology, Department of Internal Medicine, Pusan National University Yangsan Hospital, Pusan National University School of Medicine, Yangsan, Korea.
  • Daesup Lee
    Department of Emergency Medicine, Pusan National University Yangsan Hospital, Pusan National University School of Medicine, Yangsan, Korea.
  • Yun Hak Kim
    Department of Biomedical Informatics, School of Medicine, Pusan National University, Yangsan, Republic of Korea.
  • Hye Ju Yeo
    Division of Pulmonary, Allergy, and Critical Care Medicine, Department of Internal Medicine, Pusan National University Yangsan Hospital, Pusan National University School of Medicine, Yangsan, Korea.

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