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

Xing Song, Alan S. L. Yu, John A. Kellum, Lemuel R. Waitman, Michael E. Matheny, Steven Q. Simpson, Yong Hu () and Mei Liu ()
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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
Steven Q. Simpson: University of Kansas Medical Center
Yong Hu: Jinan University
Mei Liu: University of Kansas Medical Center

Nature Communications, 2020, vol. 11, issue 1, 1-12

Abstract: 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.

Date: 2020
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DOI: 10.1038/s41467-020-19551-w

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