ArticleBMC bioinformatics2023
Negation detection in Dutch clinical texts: an evaluation of rule-based and machine learning methods.
Article in BMC bioinformatics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- End-to-end pipeline for automated heart failure diagnosis with clinical notes using SNOMED-CT.Scientific reports · 2026Article
- Comorbidity Classification from Clinical Free-Text using Large Language Models: Application to Sleep Disorder Patients.Journal of medical systems · 2026Article
- Sex differences in documented clinical features of memory clinic patients: a natural language processing study.Cerebral circulation - cognition and behavior · 2026Article
- A Large Language Model to Detect Negated Expressions in Radiology Reports.Journal of imaging informatics in medicine · 2025Article
- Optimising coronary imaging decisions with machine learning: an external validation study.Open heart · 2025Article
- Mapping the Advanced-Stage Epithelial Ovarian Cancer Landscape Goes Beyond Words: Two Large Language Models, Eight Tasks, One Journey.Journal of clinical medicine · 2025Article
- Diagnosis extraction from unstructured Dutch echocardiogram reports using span- and document-level characteristic classification.BMC medical informatics and decision making · 2025Article
- Year 2023 in Biomedical Natural Language Processing: a Tribute to Large Language Models and Generative AI.Yearbook of medical informatics · 2024Article
- Annotation-preserving machine translation of English corpora to validate Dutch clinical concept extraction tools.Journal of the American Medical Informatics Association : JAMIA · 2024Article
- The added value of text from Dutch general practitioner notes in predictive modeling.Journal of the American Medical Informatics Association : JAMIA · 2023Article
- Performance and Agreement When Annotating Chest X-ray Text Reports-A Preliminary Step in the Development of a Deep Learning-Based Prioritization and Detection System.Diagnostics (Basel, Switzerland) · 2023Article
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Authors and funding
8 authors.
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Abstract
When developing models for clinical information retrieval and decision support systems, the discrete outcomes required for training are often missing. These labels need to be extracted from free text in electronic health records. For this extraction process one of the most important contextual properties in clinical text is negation, which indicates the absence of findings. We aimed to improve large scale extraction of labels by comparing three methods for negation detection in Dutch clinical notes. We used the Erasmus Medical Center Dutch Clinical Corpus to compare a rule-based method based on ContextD, a biLSTM model using MedCAT and (finetuned) RoBERTa-based models. We found that both the biLSTM and RoBERTa models consistently outperform the rule-based model in terms of F1 score, precision and recall. In addition, we systematically categorized the classification errors for each model, which can be used to further improve model performance in particular applications. Combining the three models naively was not beneficial in terms of performance. We conclude that the biLSTM and RoBERTa-based models in particular are highly accurate accurate in detecting clinical negations, but that ultimately all three approaches can be viable depending on the use case at hand.
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