用问答模型自动提取药物不良反应及关联药物，准确率达94%。
ADEQA: A Question Answer based approach for joint ADE-Suspect Extraction using Sequence-To-Sequence Transformers
- 将抽取任务转为问答形式，利用生成式模型减少标注成本。
- 在公开数据集上关系识别F1达94%，优于现有方法。
- 适合医药安全监测、药物研发等需要快速识别风险的场景。
新药上市后早期识别不良药物反应（ADE）对及时干预至关重要。这些信息分散于临床研究报告、患者病历、社交媒体等非结构化数据中。由于文本中药物与不良反应之间存在复杂的语言关系，且缺乏大规模标注数据，基于机器学习的抽取面临挑战。本文提出ADEQA，一种基于问答的联合抽取方法，结合准监督标注数据与序列到序列变换器，可同时识别不良反应、相关可疑药物及其关系。不同于传统需细粒度标注的QA模型，该方法采用自然语言生成机制，显著降低标注门槛。在公开的ADE语料库上，关系识别F1达到94%，达到当前最优水平。
原文摘要 · Abstract (English)
Early identification of Adverse Drug Events (ADE) is critical for taking prompt actions while introducing new drugs into the market. These ADEs information are available through various unstructured data sources like clinical study reports, patient health records, social media posts, etc. Extracting ADEs and the related suspect drugs using machine learning is a challenging task due to the complex linguistic relations between drug ADE pairs in textual data and unavailability of large corpus of labelled datasets. This paper introduces ADEQA, a question-answer(QA) based approach using quasi supervised labelled data and sequence-to-sequence transformers to extract ADEs, drug suspects and the relationships between them. Unlike traditional QA models, natural language generation (NLG) based models don't require extensive token level labelling and thereby reduces the adoption barrier significantly. On a public ADE corpus, we were able to achieve state-of-the-art results with an F1 score of 94% on establishing the relationships between ADEs and the respective suspects.
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