Background: Immune checkpoint inhibitors (ICIs) have revolutionized cancer
treatment but can result in severe immune-related adverse events (IrAEs).
Monitoring IrAEs on a large scale is essential for personalized risk profiling
and assisting in treatment decisions.
Methods: In this study, we conducted an analysis of clinical notes from
patients who received ICIs at the Tel Aviv Sourasky Medical Center. By
employing a Natural Language Processing algorithmic pipeline, we systematically
identified seven common or severe IrAEs. We examined the utilization of
corticosteroids, treatment discontinuation rates following IrAEs, and
constructed survival curves to visualize the occurrence of adverse events
during treatment.
Results: Our analysis encompassed 108,280 clinical notes associated with
1,635 patients who had undergone ICI therapy. The detected incidence of IrAEs
was consistent with previous reports, exhibiting substantial variation across
different ICIs. Treatment with corticosteroids varied depending on the specific
IrAE, ranging from 17.3% for thyroiditis to 57.4% for myocarditis. Our
algorithm demonstrated high accuracy in identifying IrAEs, as indicated by an
area under the curve (AUC) of 0.89 for each suspected note and F1 scores of
0.87 or higher for five out of the seven IrAEs examined at the patient level.
Conclusions: This study presents a novel, large-scale monitoring approach
utilizing deep neural networks for IrAEs. Our method provides accurate results,
enhancing understanding of detrimental consequences experienced by ICI-treated
patients. Moreover, it holds potential for monitoring other medications,
enabling comprehensive post-marketing surveillance to identify susceptible
populations and establish personalized drug safety profiles.

通过自然语言处理算法流程，针对接受免疫检查点抑制剂治疗的患者的临床记录进行分析，报道了临床记录中常见或严重的免疫相关不良事件，研究了皮质类固醇的使用情况、在不良事件发生后治疗中断率，并通过生存曲线可视化了治疗过程中不良事件的发生。