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• W4281293385 abstract "Abstract Objectives To present a model that enhances the accuracy of clinicians when presented with a possibly critical Covid-19 patient. Methods A retrospective study was performed with information of 5,745 SARS-CoV2 infected patients admitted to the Emergency room of 4 public Hospitals in Madrid belonging to Quirón Salud Health Group (QS) from March 2020 to February 2021. Demographics, clinical variables on admission, laboratory markers and therapeutic interventions were extracted from Electronic Clinical Records. Traits related to mortality were found through difference in means testing and through feature selection by learning multiple classification trees with random initialization and selecting the ones that were used the most. We validated the model through cross-validation and tested generalization with an external dataset from 4 hospitals belonging to Sanitas Hospitals Health Group. The usefulness of two different models in real cases was tested by measuring the effect of exposure to the model decision on the accuracy of medical professionals. Results Of the 5,745 admitted patients, 1,173 died. Of the 110 variables in the dataset, 34 were found to be related with our definition of criticality (death in <72 hours) or all-cause mortality. The models had an accuracy of 85% and a sensitivity of 50% averaged through 5-fold cross validation. Similar results were found when validating with data from the 4 hospitals from Sanitas. The models were found to have 11% better accuracy than doctors at classifying critical cases and improved accuracy of doctors by 12% for non-critical patients, reducing the cost of mistakes made by 17%." @default.
• W4281293385 created "2022-05-24" @default.
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• W4281293385 date "2022-05-10" @default.
• W4281293385 modified "2023-10-01" @default.
• W4281293385 title "Decision trees for COVID-19 prognosis learned from patient data: Desaturating the ER with Artificial Intelligence" @default.
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• W4281293385 doi "https://doi.org/10.1101/2022.05.09.22274832" @default.
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