Savable but Lost Lives when ICU Is Overloaded: a Model from 733 Patients in Epicenter Wuhan, China

نویسندگان

چکیده

Coronavirus Disease 2019 (COVID-19) causes a sudden turnover to bad at some checkpoints and thus needs the intervention of intensive care unit (ICU). This resulted in urgent large ICUs posed great risks medical system. Estimating mortality critical in-patients who were not admitted into ICU will be valuable optimize management assignment ICU. Retrospective, 733 diagnosed with COVID-19 local hospital (Wuhan, China), as March 18, 2020. Demographic, clinical laboratory results collected analyzed using machine learning build predictive model. Considering shortage beds beginning disease emergence, we defined for those patients predicted needing yet they did Missing-ICU (MI)-mortality. To estimate MI-mortality, prognostic classification model was built identify may need care. Its accuracy 0.8288, an AUC 0.9119. On our cohort patients, 25 have been by that should ICU, enter due lack shorting wards. Our analysis had shown MI-mortality is 41%, 32%, implying enough bed treating conditions.

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ژورنال

عنوان ژورنال: Proceedings of the ... AAAI Conference on Artificial Intelligence

سال: 2021

ISSN: ['2159-5399', '2374-3468']

DOI: https://doi.org/10.1609/aaai.v35i6.16612