An artificial intelligence approach for predicting death or organ failure after hospitalization for...
An artificial intelligence approach for predicting death or organ failure after hospitalization for COVID-19: development of a novel risk prediction tool and comparisons with ISARIC-4C, CURB-65, qSOFA, and MEWS scoring systems
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Author / Creator
Kwok, Stephen Wai Hang , Wang, Guanjin , Sohel, Ferdous , Kashani, Kianoush B , Zhu, Ye , Wang, Zhen , Antpack, Eduardo , Khandelwal, Kanika , Pagali, Sandeep R , Nanda, Sanjeev , Abdalrhim, Ahmed D , Sharma, Umesh M , Bhagra, Sumit , Dugani, Sagar , Takahashi, Paul Y , Murad, Mohammad H and Yousufuddin, Mohammed
Publisher
England: BioMed Central Ltd
Journal title
Language
English
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Publisher
England: BioMed Central Ltd
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Scope and Contents
Contents
We applied machine learning (ML) algorithms to generate a risk prediction tool [Collaboration for Risk Evaluation in COVID-19 (CORE-COVID-19)] for predicting the composite of 30-day endotracheal intubation, intravenous administration of vasopressors, or death after COVID-19 hospitalization and compared it with the existing risk scores.
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Alternative Titles
Full title
An artificial intelligence approach for predicting death or organ failure after hospitalization for COVID-19: development of a novel risk prediction tool and comparisons with ISARIC-4C, CURB-65, qSOFA, and MEWS scoring systems
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Record Identifier
TN_cdi_doaj_primary_oai_doaj_org_article_9518aa5cbe204f4181349ef94bfc8e69
Permalink
https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_doaj_primary_oai_doaj_org_article_9518aa5cbe204f4181349ef94bfc8e69
Other Identifiers
ISSN
1465-993X,1465-9921
E-ISSN
1465-993X,1465-9921
DOI
10.1186/s12931-023-02386-6
