Calibrated AI approach to pharmacovigilance using FAERS

IF 18 1区医学 Q1 HEALTH CARE SCIENCES & SERVICES

NPJ Digital Medicine Pub Date : 2026-10-06 DOI:10.1038/s41746-026-03273-2

Zhouzhou Chu, Sherry Yun Wang

引用次数: 0

Abstract

We developed a transparent, calibrated AI framework using 242,312 FAERS reports on GLP-1 receptor agonists to predict adverse event seriousness. All models achieved strong discrimination, with area under the receiver operating characteristic curve (ROC-AUC)ranging from 0.877 to 0.930, and post hoc calibration improved probability reliability. Frequently reported gastrointestinal events showed low predictive importance, whereas dosing/administration, injection-site, and pancreatic events were among the strongest predictors, highlighting a disconnect between reporting frequency and predictive importance for reporter-classified seriousness.

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使用FAERS进行药物警戒的校准人工智能方法

我们利用242,312份关于GLP-1受体激动剂的FAERS报告，开发了一个透明的、校准的AI框架，以预测不良事件的严重程度。所有模型均具有较强的识别能力，受试者工作特征曲线下面积（ROC-AUC）在0.877 ~ 0.930之间，事后校正提高了概率可靠性。频繁报告的胃肠道事件显示出较低的预测重要性，而剂量/给药、注射部位和胰腺事件是最强的预测因素，突出了报告频率和报告者分类严重性的预测重要性之间的脱节。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

NPJ Digital Medicine Multiple-

CiteScore

25.10

自引率

3.30%

发文量

170

审稿时长

15 weeks

期刊介绍： npj Digital Medicine is an online open-access journal that focuses on publishing peer-reviewed research in the field of digital medicine. The journal covers various aspects of digital medicine, including the application and implementation of digital and mobile technologies in clinical settings, virtual healthcare, and the use of artificial intelligence and informatics. The primary goal of the journal is to support innovation and the advancement of healthcare through the integration of new digital and mobile technologies. When determining if a manuscript is suitable for publication, the journal considers four important criteria: novelty, clinical relevance, scientific rigor, and digital innovation.