Medicine and Health

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

X. Song, A. S. L. Yu, et al.

This groundbreaking research by Xing Song and colleagues leverages artificial intelligence to predict acute kidney injury (AKI), revealing challenges in clinical adoption due to varying risk factors across different health systems. The findings not only highlight performance issues but also propose a novel method to enhance AI model transportability and adaptation in hospitals.

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Abstract

Artificial intelligence (AI) shows promise in predicting acute kidney injury (AKI), but clinical adoption needs interpretability and transportability. This study uses the US PCORnet platform to develop an AKI prediction model and assess its transportability across six health systems. Results show cross-site performance deterioration, caused by heterogeneity of risk factors. A method to predict AI model transportability is derived, accelerating external AI model adaptation in hospitals.

Publisher

Nature Communications

Published On

Nov 09, 2020

Authors

Xing Song, Alan S. L. Yu, John A. Kellum, Lemuel R. Waitman, Michael E. Matheny, Steven Q. Simpson, Yong Hu, Mei Liu

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