Article

Extracting adverse event nature, severity, timelines and resulting interventions from clinical notes of patients receiving CAR-T therapy using large language models

2026-05-05

Abstract excerpt

Chimeric Antigen Receptor T-cell (CAR-T) therapy, where genetically engineered patient T cells target tumor antigens, has transformed care for hematologic malignancies but requires careful tracking of adverse events (AEs) often documented only in unstructured EHR notes. We evaluated a Large Language Model (LLM)–based approach in UCSF’s secure environment to extract AEs, dates, grades, and interventions within 30 d...

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Literature Corpus work: ba2292ee-ddd0-5d40-b665-d2581e216f71
DOI: 10.64898/2026.04.28.26351782

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