In this manuscript, we test the hypothesis that machine learning methods previously used in adults [20] could be applied to available bedside clinical variables including C-reactive protein and ferritin in the extant PHENOMS dataset [15] to derive 24-h computable sepsis phenotypes [20–22] that identify children at risk for development of TAMOF and MAS for enrollment in early personalized anti-thrombotic and anti-inflammatory clinical trials. The gene discussed is CRP; the disease is Sepsis.