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

Miguel Angel Rios Gaona, Bram van Es (Korresp. Autor*in), Leon C. Reteig, Sander C. Tan, Marijn Schraagen, Myrthe M. Hemker, Sebastiaan R.S. Arends, Saskia Haitjema

Veröffentlichungen: Beitrag in FachzeitschriftArtikelPeer Reviewed

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.
OriginalspracheEnglisch
Aufsatznummer10
FachzeitschriftBMC Bioinformatics
Jahrgang24
Ausgabenummer1
DOIs
PublikationsstatusVeröffentlicht - 9 Jan. 2023

ÖFOS 2012

  • 102020 Medizinische Informatik
  • 102001 Artificial Intelligence

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