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- Title
Cross-site transportability of an explainable artificial intelligence model for acute kidney injury prediction.
- Authors
Song, Xing; Yu, Alan S. L.; Kellum, John A.; Waitman, Lemuel R.; Matheny, Michael E.; Simpson, Steven Q.; Hu, Yong; Liu, Mei
- 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. Artificial intelligence (AI) has demonstrated promise in predicting acutekidney injury (AKI), however, clinical adoption of these models requires interpretability and transportability across sites. Here, the authors develop an AKI prediction model and a measure for model transportability across six independent health systems.
- Subjects
ACUTE kidney failure; ARTIFICIAL intelligence; FORECASTING; PREDICTION models
- Publication
Nature Communications, 2020, Vol 11, Issue 1, pN.PAG
- ISSN
2041-1723
- Publication type
Article
- DOI
10.1038/s41467-020-19551-w