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Predicting progression to septic shock in the emergency department using an externally generalizable machine learning algorithm
OBJECTIVE: Machine-learning (ML) algorithms allow for improved prediction of sepsis syndromes in the ED using data from electronic medical records. Transfer learning, a new subfield of ML, allows for generalizability of an algorithm across clinical sites. We aimed to validate the Artificial Intellig...
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| Опубликовано в: : | medRxiv |
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| Главные авторы: | , , , , , |
| Формат: | Artigo |
| Язык: | Inglês |
| Опубликовано: |
Cold Spring Harbor Laboratory
2020
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| Предметы: | |
| Online-ссылка: | https://ncbi.nlm.nih.gov/pmc/articles/PMC7654881/ https://ncbi.nlm.nih.gov/pubmed/33173889 https://ncbi.nlm.nih.govhttp://dx.doi.org/10.1101/2020.11.02.20224931 |
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