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 [BibTeX] [Marc21]
Meta-analysis informed machine learning: Supporting cytokine storm detection during CAR-T cell Therapy
Type of publication: Journal paper
Publication status: Published
Journal: Journal of Biomedical Informatics
Volume: 142
Year: 2023
Month: June
DOI: https://doi.org/10.1016/j.jbi.2023.104367
Abstract: 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 otherwise 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. To this end we also address a widespread challenge of the applicability of machine learning in general: reduced training data availability. We do so by augmenting available (but often insufficient) patient cytokine concentrations with statistical knowledge extracted from domain literature. We argue that such methods could support clinicians in analyzing suspect cytokine profiles by matching them against the said CRS knowledge from past clinical studies, with the ultimate aim of swift CRS diagnosis. We evaluate our proposed methods under several design choices, achieving performance of more than 90% in terms of CRS identification accuracy, and showing that many of our choices outperform a purely data-driven alternative. 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.
Keywords: Cytokine stormExplainable AIHealthcare predictive analysisMachine learning for diagnosis
Projects Idiap
Authors Bogatu, Alex
Wysocka, Magdalena
Wysocki, Oskar
Butterworth, Holly
Pillai, Manon
Allison, Jennifer
Landers, Donal
Kilgour, Elaine
Thistlethwaite, Fiona
Freitas, Andre
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