构建老年人出院记录不良事件抽取数据集，支持复杂标注与罕见事件识别。
Adverse Event Extraction from Discharge Summaries: A New Dataset, Annotation Scheme, and Initial Findings
- 设计支持重叠和不连续实体的标注方案，提升临床语义表达能力。
- 粗粒度任务F1达0.943，细粒度仅0.675，罕见事件识别仍存挑战。
- 专为老年患者设计，适合医疗NLP研究者评估模型泛化能力。
本文构建了一个针对老年患者出院记录的不良事件（AE）抽取人工标注语料库，涵盖跌倒、谵妄、颅内出血等14类临床重要不良事件，并包含否定、诊断类型、院内发生等上下文属性。标注方案首次支持不连续与重叠实体，解决以往研究较少关注的难题。在三个标注粒度上评估FlairNLP模型：基于Transformer的BERT-cased在文档级粗粒度任务中表现优异（F1=0.943），但在实体级细粒度任务中显著下降（如F1=0.675），尤其对罕见事件和复杂属性识别效果不佳。结果表明，尽管整体得分高，检测代表性不足的不良事件与捕捉细微临床语言仍是关键挑战。该数据集在可信研究环境（TRE）内开发，可通过DataLoch申请获取，为不良事件抽取方法评估及跨数据集泛化提供可靠基准。
原文摘要 · Abstract (English)
In this work, we present a manually annotated corpus for Adverse Event (AE) extraction from discharge summaries of elderly patients, a population often underrepresented in clinical NLP resources. The dataset includes 14 clinically significant AEs-such as falls, delirium, and intracranial haemorrhage, along with contextual attributes like negation, diagnosis type, and in-hospital occurrence. Uniquely, the annotation schema supports both discontinuous and overlapping entities, addressing challenges rarely tackled in prior work. We evaluate multiple models using FlairNLP across three annotation granularities: fine-grained, coarse-grained, and coarse-grained with negation. While transformer-based models (e.g., BERT-cased) achieve strong performance on document-level coarse-grained extraction (F1 = 0.943), performance drops notably for fine-grained entity-level tasks (e.g., F1 = 0.675), particularly for rare events and complex attributes. These results demonstrate that despite high-level scores, significant challenges remain in detecting underrepresented AEs and capturing nuanced clinical language. Developed within a Trusted Research Environment (TRE), the dataset is available upon request via DataLoch and serves as a robust benchmark for evaluating AE extraction methods and supporting future cross-dataset generalisation.
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