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ASH 2023 | Using machine learning to identify trajectories of hematotoxicity following CAR T-cell therapy
Emily Liang, MD, Fred Hutchinson Cancer Center, Seattle, WA, describes a machine learning-based study which aimed to automate the identification of distinct trajectories of hematotoxicity following CAR T-cell therapy in patients with hematological malignancies. This was achieved by using time-series-based clustering of longitudinal absolute neutrophil count (ANC) data from over 400 patients treated with CAR T-cell therapy, and from this, four distinct trajectories were defined. The study also identified factors associated with each trajectory and assessed the predictive ability of this method against that of the CAR-HEMATOTOX score. This interview took place at the 65th ASH Annual Meeting and Exposition, held in San Diego, CA.
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