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Predictive modeling of morbidity and mortality in COVID-19 hospitalized patients and its clinical implications.
Clinical activity of 3740 de-identified COVID-19 positive patients treated at NYU Langone Health (NYULH) were collected between January and August 2020. XGBoost model trained on clinical data from the final 24 hours excelled at predicting mortality (AUC=0.92, specificity=86% and sensitivity=85%). Re...
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| Pubblicato in: | medRxiv |
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| Autori principali: | , , , , , |
| Natura: | Artigo |
| Lingua: | Inglês |
| Pubblicazione: |
Cold Spring Harbor Laboratory
2021
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| Soggetti: | |
| Accesso online: | https://ncbi.nlm.nih.gov/pmc/articles/PMC7724684/ https://ncbi.nlm.nih.gov/pubmed/33300013 https://ncbi.nlm.nih.govhttp://dx.doi.org/10.1101/2020.12.02.20235879 |
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