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

As structured data are often insufficient, labels need to be extracted from free text in electronic health records when developing models for clinical information retrieval and decision support systems. 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)... Mehr ...

Verfasser: van Es, Bram
Reteig, Leon C.
Tan, Sander C.
Schraagen, Marijn
Hemker, Myrthe M.
Arends, Sebastiaan R. S.
Rios, Miguel A. R.
Haitjema, Saskia
Dokumenttyp: Artikel
Erscheinungsdatum: 2022
Verlag/Hrsg.: arXiv
Schlagwörter: Computation and Language cs.CL / Information Retrieval cs.IR / Machine Learning cs.LG / Machine Learning stat.ML / FOS: Computer and information sciences / I.2.7; J.3; H.3.3 / 68T50 / 68P20
Sprache: unknown
Permalink: https://search.fid-benelux.de/Record/base-28980402
Datenquelle: BASE; Originalkatalog
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Link(s) : https://dx.doi.org/10.48550/arxiv.2209.00470