https://www.selleckchem.com/pr....oducts/bgb-3245-brim
We propose a method for screening full blood count metadata for evidence of communicable and noncommunicable diseases using machine learning (ML). High dimensional hematology metadata was extracted over an 11-month period from Sysmex hematology analyzers from 43,761 patients. Predictive models for age, **** and individuality were developed to demonstrate the personalized nature of hematology data. Both numeric and raw flow cytometry data were used for both supervised and unsupervised ML to predict the presence of pneumonia,
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