Elsaid Mohamed I, Meara Alexa Simon, Owen Dwight H
Department of Biomedical Informatics, College of Medicine, The Ohio State University, Columbus, OH.
Division of Medical Oncology, College of Medicine, The Ohio State University, Columbus, OH.
J Clin Oncol. 2024 Dec 10;42(35):4119-4122. doi: 10.1200/JCO-24-01570. Epub 2024 Oct 2.
In the article that accompanies this editorial, Sun and colleagues utilized large language models (LLMs) to detect immune-related adverse events (irAEs) from electronic health records and demonstrated that LLMs had higher sensitivity than ICD codes alone (94.7% vs 68.7% respectively) and similar specificity, requiring a fraction of the time compared to manual adjudication. This research represents a significant step in enhancing the efficiency of our efforts to better understand irAEs by leveraging data in the electronic medical records from patients treated with immune checkpoint inhibitors over the last 15 years to better predict these toxicities, with the goal of minimizing or mitigating them entirely.
在这篇社论所附的文章中，孙及其同事利用大语言模型（LLMs）从电子健康记录中检测免疫相关不良事件（irAEs），并证明大语言模型比单独使用国际疾病分类（ICD）编码具有更高的敏感性（分别为94.7%和68.7%）以及相似的特异性，与人工判定相比所需时间仅为一小部分。这项研究代表了一个重要的进展，即通过利用过去15年接受免疫检查点抑制剂治疗患者的电子病历数据，提高我们更好地理解irAEs的效率，从而更好地预测这些毒性反应，目标是将其降至最低或完全缓解。
