  Cytokine release syndrome (CRS), also known as cytokine storm, is one of the
most consequential adverse effects of chimeric antigen receptor therapies that
have shown promising results in cancer treatment. When emerging, CRS could be
identified by the analysis of specific cytokine and chemokine profiles that
tend to exhibit similarities across patients. In this paper, we exploit these
similarities using machine learning algorithms and set out to pioneer a
meta--review informed method for the identification of CRS based on specific
cytokine peak concentrations and evidence from previous clinical studies. We
argue that such methods could support clinicians in analyzing suspect cytokine
profiles by matching them against CRS knowledge from past clinical studies,
with the ultimate aim of swift CRS diagnosis. During evaluation with
real--world CRS clinical data, we emphasize the potential of our proposed
method of producing interpretable results, in addition to being effective in
identifying the onset of cytokine storm.
