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An Interpretable Machine Learning Model for Accurate Prediction of Sepsis in the ICU
OBJECTIVE: Sepsis is among the leading causes of morbidity, mortality, and cost overruns in critically ill patients. Early intervention with antibiotics improves survival in septic patients. However, no clinically validated system exists for real-time prediction of sepsis onset. We aimed to develop...
Tallennettuna:
| Julkaisussa: | Crit Care Med |
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| Päätekijät: | , , , , , |
| Aineistotyyppi: | Artigo |
| Kieli: | Inglês |
| Julkaistu: |
2018
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| Aiheet: | |
| Linkit: | https://ncbi.nlm.nih.gov/pmc/articles/PMC5851825/ https://ncbi.nlm.nih.gov/pubmed/29286945 https://ncbi.nlm.nih.govhttp://dx.doi.org/10.1097/CCM.0000000000002936 |
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