Influence of Context in Transformer-Based Medication Relation Extraction
The extraction of medication information from unstructured clinical documents has been a major application of clinical NLP in the past decade as evidenced by the conduct of two shared tasks under the I2B2 and N2C2 umbrella. We here propose a new methodological approach which has already shown a trem...
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| Published in: | Studies in health technology and informatics Vol. 310; p. 669 |
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| Main Authors: | , |
| Format: | Journal Article |
| Language: | English |
| Published: |
Netherlands
25-01-2024
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| Subjects: | |
| Online Access: | Get more information |
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| Summary: | The extraction of medication information from unstructured clinical documents has been a major application of clinical NLP in the past decade as evidenced by the conduct of two shared tasks under the I2B2 and N2C2 umbrella. We here propose a new methodological approach which has already shown a tremendous potential for increasing system performance for general NLP tasks, but has so far not been applied to medication extraction from EHR data, namely deep learning based on transformer models. We ran experiments on established clinical data sets for English (exploiting I2B2 and N2C2 corpora) and German (based on the 3000PA corpus, a German reference data set). Our results reveal that transformer models are on a par with current state-of-the-art results for English, but yield new ones for German data. We further address the influence of context on the overall performance of transformer-based medication relation extraction. |
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| ISSN: | 1879-8365 |
| DOI: | 10.3233/SHTI231049 |