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A.I. analytics of laboratory variables to predict clinical outcome after Covid-19

Using real world, serial data from the electronic health records of 3500 confirmed COVID positive pateints at UHB, researchers at the University of Birmingham will:
-increase the predictive power of clinical outcome for patients with Covid-19 based on incorporation of laboratory values into risk analyses using random forest modelling
-reveal novel biological insights into the pathogenesis of the disease by assessment of the significant of laboratory variables in isolation and also in relation to other values
-increase, and potentially contribute to, the scale and power of the team undertaking clinical informatic research on Covid-19 within BHP

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