AI Article Synopsis
- The study focuses on creating a machine learning model to predict the mortality and hospitalization risk of COVID-19 patients using minimal data from electronic medical records.
- The model shows high accuracy in predicting outcomes, particularly regarding death (90-93%) and a medium level of accuracy for hospitalization risk (71-73%).
- Key factors influencing predictions include age, sex, comorbidities, and a user-friendly website has been created for clinicians to easily access the model's predictions.
Article Abstract
The development of tools that provide early triage of COVID-19 patients with minimal use of diagnostic tests, based on easily accessible data, can be of vital importance in reducing COVID-19 mortality rates during high-incidence scenarios. This work proposes a machine learning model to predict mortality and risk of hospitalization using both 2 simple demographic features and 19 comorbidities obtained from 86,867 electronic medical records of COVID-19 patients, and a new method (LR-IPIP) designed to deal with data imbalance problems. The model was able to predict with high accuracy (90-93%, ROC-AUC = 0.94) the patient's final status (deceased or discharged), while its accuracy was medium (71-73%, ROC-AUC = 0.75) with respect to the risk of hospitalization. The most relevant characteristics for these models were age, sex, number of comorbidities, osteoarthritis, obesity, depression, and renal failure. Finally, to facilitate its use by clinicians, a user-friendly website has been developed ( https://alejandrocisterna.shinyapps.io/PROVIA ).
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Source |
|---|---|
| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC9614188 | PMC |
| http://dx.doi.org/10.1038/s41598-022-22547-9 | DOI Listing |
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