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Negation detection in Dutch clinical texts: an evaluation of rule-based and machine learning methods

info:eu-repo/semantics/OpenAccess ;ISSN: 1471-2105 ;EISSN: 1471-2105

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  • Title:
    Negation detection in Dutch clinical texts: an evaluation of rule-based and machine learning methods
  • Author: van Es, Bram ; Reteig, Leon ; Tan, Sander ; Schraagen, Marijn ; Hemker, Myrthe ; Arends, Sebastiaan ; Rios, Miguel ; Haitjema, Saskia
  • Subjects: Natural language processing ; Negation detection ; Text mining
  • Description: 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.
  • Creation Date: 2023-12
  • Language: English
  • Identifier: ISSN: 1471-2105
    EISSN: 1471-2105
  • Source: SpringerOpen
    PubMed Central (Open access)
    Geneva Foundation Free Medical Journals at publisher websites
    Directory of Open Access Journals
    ROAD: Directory of Open Access Scholarly Resources
    ProQuest Central
    Utrecht University Repository
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