Kunz, Sebastian; Zgraggen, Cyril; Sariyar, Murat (2022). Mapping SNOMED CT Codes to Semi-Structured Texts via an NLP Pipeline Studies in Health Technology and Informatics, 295, pp. 390-393. IOS Press 10.3233/SHTI220747
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In the project presented here, we used NLP tools for annotating German medical trainings documents with SNOMED CT codes. Following research question was addressed: Is it possible to automate the annotation of training documents with an NLP pipeline especially designed for this task but requiring translation into English? The goal of our stakeholder, an institution responsible for the continuing education of physicians, was to facilitate the switch between different medical trainings programs by coding the same requirement with the same SNOMED CT code, even if the wording is different. We first describe how we chose the concrete NLP tools, after which the concrete steps for implementing our prototype are outlined: the NLP pipeline construction, the implementation, and the validation. We infer three important lessons from our results: (i) self-supervision is no free lunch and should be based on a sophisticated task, (ii) the translation via DeepL can be too context-dependent for a peculiar use case, and (iii) ontology extraction can increase efficiency as well as accuracy.
Item Type: |
Journal Article (Original Article) |
|---|---|
Division/Institute: |
School of Engineering and Computer Science > Institut für Medizininformatik I4MI School of Engineering and Computer Science |
Name: |
Kunz, Sebastian; Zgraggen, Cyril and Sariyar, Murat |
Subjects: |
Q Science > QA Mathematics > QA75 Electronic computers. Computer science |
ISSN: |
1879-8365 |
ISBN: |
9781643682907 |
Series: |
Studies in Health Technology and Informatics |
Publisher: |
IOS Press |
Language: |
English |
Submitter: |
Murat Sariyar |
Date Deposited: |
20 Dec 2022 15:32 |
Last Modified: |
15 Jan 2024 15:26 |
Publisher DOI: |
10.3233/SHTI220747 |
ARBOR DOI: |
10.24451/arbor.18475 |
URI: |
https://arbor.bfh.ch/id/eprint/18475 |
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