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Predicting the outcome for patients in a heart transplantation queue using deep learning.

Dennis Medved, Pierre Nugues, Johan Nilsson

    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
    |October 25, 2017
    PubMed
    Summary

    Deep learning models predict heart transplant waiting list outcomes. This approach improves patient status prediction, offering valuable insights for managing organ transplant waiting lists.

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    Area of Science:

    • Medical Informatics
    • Cardiology
    • Machine Learning

    Background:

    • Heart transplantation offers extended survival for end-stage heart disease but is limited by donor organ scarcity.
    • Patients face prolonged waiting periods, averaging 200 days, with significant individual variability.
    • Predicting patient outcomes during the waiting period is crucial for resource allocation and patient management.

    Purpose of the Study:

    • To develop and evaluate deep learning models for predicting patient outcomes on the heart transplant waiting list.
    • To assess model performance at multiple time points (180, 365, and 730 days).
    • To identify key predictors influencing patient status during the waiting period.

    Main Methods:

    • Utilized a two-layer neural network architecture implemented with the Keras framework.

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  • Trained the model on adult patient data (>17 years) from the United Network for Organ Sharing (UNOS) registry (January 2000 - December 2011).
  • Employed a backward elimination procedure to determine the 10 most significant predictive parameters.

    • Main Results:

    • Achieved F1 macro scores of 0.674, 0.680, and 0.680 at 180, 365, and 730 days, respectively.
    • Demonstrated a significant improvement over a baseline model with a score of 0.271.
    • Identified the top 10 parameters most influential in predicting patient outcomes.

    Conclusions:

    • Deep learning models can effectively predict patient outcomes on heart transplant waiting lists.
    • The identified significant parameters offer insights into factors affecting patient survival and transplantation status.
    • This predictive capability can aid in optimizing waiting list management and patient care strategies.