OnSIDES database: Extracting adverse drug events from drug labels using natural language processing models.

IF 12.8 Q1 MEDICINE, RESEARCH & EXPERIMENTAL

Med Pub Date : 2025-03-27 DOI:10.1016/j.medj.2025.100642

Yutaro Tanaka, Hsin Yi Chen, Pietro Belloni, Undina Gisladottir, Jenna Kefeli, Jason Patterson, Apoorva Srinivasan, Michael Zietz, Gaurav Sirdeshmukh, Jacob Berkowitz, Kathleen LaRow Brown, Nicholas P Tatonetti

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引用次数: 0

Abstract

Background: Adverse drug events (ADEs) are the fourth leading cause of death in the US and cost billions of dollars annually in increased healthcare costs. However, few machine-readable databases of ADEs exist, limiting our capacity to study drug safety on a broader, systematic scale. Recent advances in natural language processing methods, such as BERT models, present an opportunity to accurately extract relevant information from unstructured biomedical text.

Methods: We fine-tune a PubMedBERT model to extract ADE terms from text in FDA Structured Product Labels for prescription drugs. Here, we present OnSIDES (on-label side effects resource), a compiled, machine-friendly database of drug-ADE pairs generated with this method. We further utilize this method to extract pediatric-specific ADEs, serious ADEs from labels' "Boxed Warnings" section, and ADEs from drug labels of other major nations-the UK, the European Union, and Japan-to build a complementary OnSIDES-INTL database. To present OnSIDES' potential applications, we leverage the database to predict novel drug targets and indications, analyze enrichment of ADEs across drug classes, and predict novel ADEs from chemical compound structures.

Findings: We achieve an F1 score of 0.90, AUROC of 0.92, and AUPR of 0.95 at extracting ADEs from the labels' "Adverse Reactions" section. OnSIDES contains over 3.6 million drug-ADE pairs for 3,233 unique drug ingredient combinations extracted from 47,211 labels.

Conclusions: OnSIDES can be used as a comprehensive resource to study and enhance drug safety.

Funding: R35GM131905 to N.P.T.; T32GM145440 to H.Y.C.; and T15LM007079 to U.G., M.Z., and K.L.B.

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背景：药物不良事件（ADEs）是美国第四大死因，每年增加的医疗成本高达数十亿美元。然而，现有的机器可读 ADE 数据库很少，这限制了我们在更广泛、更系统的范围内研究药物安全性的能力。自然语言处理方法（如 BERT 模型）的最新进展为从非结构化生物医学文本中准确提取相关信息提供了机会：我们对 PubMedBERT 模型进行了微调，以便从 FDA 处方药结构化产品标签的文本中提取 ADE 术语。在此，我们介绍了OnSIDES（标签上副作用资源），这是一个由该方法生成的药物-ADE对的机器友好型编译数据库。我们进一步利用这种方法提取了儿科特定的 ADE、标签 "警示框 "部分的严重 ADE 以及其他主要国家（英国、欧盟和日本）药品标签中的 ADE，从而建立了一个互补的 OnSIDES-INTL 数据库。为了展示 OnSIDES 的潜在应用，我们利用该数据库预测了新型药物靶点和适应症，分析了不同药物类别中 ADE 的富集情况，并从化合物结构中预测了新型 ADE：我们从标签的 "不良反应 "部分提取 ADE 的 F1 得分为 0.90，AUROC 为 0.92，AUPR 为 0.95。OnSIDES 包含从 47,211 个标签中提取的 3,233 种独特药物成分组合的 360 多万个药物-ADE 对：OnSIDES 可作为研究和提高药物安全性的综合资源：R35GM131905 给 N.P.T.；T32GM145440 给 H.Y.C.；T15LM007079 给 U.G.、M.Z.和 K.L.B.。

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来源期刊

Med MEDICINE, RESEARCH & EXPERIMENTAL-

CiteScore

17.70

自引率

0.60%

发文量

102

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