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Cross-site transportability of an explainable artificial intelligence model for acute kidney injury prediction
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 PCO...
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| 出版年: | Nat Commun |
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| 主要な著者: | , , , , , , , |
| フォーマット: | Artigo |
| 言語: | Inglês |
| 出版事項: |
Nature Publishing Group UK
2020
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| 主題: | |
| オンライン･アクセス: | https://ncbi.nlm.nih.gov/pmc/articles/PMC7653032/ https://ncbi.nlm.nih.gov/pubmed/33168827 https://ncbi.nlm.nih.govhttp://dx.doi.org/10.1038/s41467-020-19551-w |
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