Adverse Event Extraction from Discharge Summaries: A New Dataset, Annotation Scheme, and Initial Findings

• URL: http://arxiv.org/abs/2506.14900v1
• Date: Tue, 17 Jun 2025 18:13:40 GMT
• Title: Adverse Event Extraction from Discharge Summaries: A New Dataset, Annotation Scheme, and Initial Findings
• Authors: Imane Guellil, Salomé Andres, Atul Anand, Bruce Guthrie, Huayu Zhang, Abul Hasan, Honghan Wu, Beatrice Alex,
• Abstract summary: We present a manually annotated corpus for Adverse Event (AE) extraction from discharge summaries of elderly patients.<n>The dataset includes 14 clinically significant AEs-such as falls, delirium, and intracranial haemorrhage.<n>We evaluate multiple models using FlairNLP across three annotation granularities.
• Score: 1.9036581654832787
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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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