Using artificial intelligence approaches, we developed four independent ML models, an EM, and a point-based CORE-COVID-19 risk prediction tool with discrimination and net clinical benefits analysis. The results demonstrated that the ML models and the CORE-COVID-19 model were consistently superior to four existing risk prediction tools for predicting the 30-day composite of death or organ failure in patients hospitalized for first-ever COVID-19 with a broader clinical spectrum.
The feature selection process identified the six most predictive variables from multiple domains from a total of 92 potential candidate predictors. The six selected variables were used to train ML models and to develop a new 16-point-based CORE-COVID-19 model. Of the six variables, admitting service was a hospital level characteristic. The Mayo Clinic with its 16 hospitals across four states is a highly integrated and closely regulated healthcare system in the United States.
Compared with the ML-derived CORE-COVID-19 model, the modest performance of existing tools could lead to underestimation of the risk, consequent inappropriate interventions, and sub-optimal outcomes. In contrast, the CORE-COVID-19 model improved the precision classification between COVID-19 patients with and without the composite outcome.
Source: Education Headlines (educationheadlines.net)
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