Machine learning for post-liver transplant survival: Bridging the gap for long-term outcomes through temporal variation features.

Journal: Computer methods and programs in biomedicine
PMID: 39368442

Abstract

BACKGROUND: The long-term survival of liver transplant (LT) recipients is essential for optimizing organ allocation and estimating mortality outcomes. While models like the Model-for-End-Stage-Liver-Disease (MELD) predict 90-day mortality on the waiting list, they do not predict post-LT survival accurately. There is a need for predictive models that can forecast post-LT survival beyond the immediate period after transplantation.

Authors

  • Kiruthika Balakrishnan
    Department of Family, Community and Health Systems Science, University of Florida, Gainesville, FL, USA.
  • Sawyer Olson
    School of Statistics, University of Minnesota, MN, USA.
  • Gyorgy Simon
    University of Minnesota, Institute for Health Informatics.
  • Lisiane Pruinelli
    Institute for Health Informatics, University of Minnesota, Minneapolis, MN.

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