Negation detection in Dutch clinical texts: an evaluation of rule-based and machine learning methods
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 metho... Mehr ...
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Dokumenttyp: | Artikel |
Erscheinungsdatum: | 2023 |
Reihe/Periodikum: | BMC Bioinformatics, Vol 24, Iss 1, Pp 1-20 (2023) |
Verlag/Hrsg.: |
BMC
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Schlagwörter: | Natural language processing / Text mining / Negation detection / Computer applications to medicine. Medical informatics / R858-859.7 / Biology (General) / QH301-705.5 |
Sprache: | Englisch |
Permalink: | https://search.fid-benelux.de/Record/base-26630201 |
Datenquelle: | BASE; Originalkatalog |
Powered By: | BASE |
Link(s) : | https://doi.org/10.1186/s12859-022-05130-x |