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Cross-site transportability of an explainable artificial intelligence model for acute kidney injury prediction

Author

Listed:
  • Xing Song

    (University of Kansas Medical Center)

  • Alan S. L. Yu

    (University of Kansas Medical Center)

  • John A. Kellum

    (University of Pittsburgh School of Medicine)

  • Lemuel R. Waitman

    (University of Kansas Medical Center)

  • Michael E. Matheny

    (Vanderbilt University School of Medicine
    Geriatrics Research Education and Clinical Care Center, Tennessee Valley Healthcare System VA)

  • Steven Q. Simpson

    (University of Kansas Medical Center)

  • Yong Hu

    (Jinan University)

  • Mei Liu

    (University of Kansas Medical Center)

Abstract

Artificial intelligence (AI) has demonstrated promise in predicting acute kidney injury (AKI), however, clinical adoption of these models requires interpretability and transportability. Non-interoperable data across hospitals is a major barrier to model transportability. Here, we leverage the US PCORnet platform to develop an AKI prediction model and assess its transportability across six independent health systems. Our work demonstrates that cross-site performance deterioration is likely and reveals heterogeneity of risk factors across populations to be the cause. Therefore, no matter how accurate an AI model is trained at the source hospital, whether it can be adopted at target hospitals is an unanswered question. To fill the research gap, we derive a method to predict the transportability of AI models which can accelerate the adaptation process of external AI models in hospitals.

Suggested Citation

  • Xing Song & Alan S. L. Yu & John A. Kellum & Lemuel R. Waitman & Michael E. Matheny & Steven Q. Simpson & Yong Hu & Mei Liu, 2020. "Cross-site transportability of an explainable artificial intelligence model for acute kidney injury prediction," Nature Communications, Nature, vol. 11(1), pages 1-12, December.
    • Handle: RePEc:nat:natcom:v:11:y:2020:i:1:d:10.1038_s41467-020-19551-w
    DOI: 10.1038/s41467-020-19551-w
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    1. Lu, Chujie & Li, Sihui & Reddy Penaka, Santhan & Olofsson, Thomas, 2023. "Automated machine learning-based framework of heating and cooling load prediction for quick residential building design," Energy, Elsevier, vol. 274(C).

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