Clinical NER model using spaCy's SpanCategorizer implementation and medBERT.de.
Usage:
!huggingface-cli download phlobo/de_ggponc_medbertde de_ggponc_medbertde-1.0.0-py3-none-any.whl --local-dir .
!pip install de_ggponc_medbertde-1.0.0-py3-none-any.whl
import spacy
nlp = spacy.load('de_ggponc_medbertde')
d = nlp("allein nach Versagen einer Behandlung mit Oxaliplatin und Irinotecan")
for e in d.spans['entities']:
print(e, e.label_)
yields:
Oxaliplatin Clinical_Drug
Irinotecan Clinical_Drug
Versagen einer Behandlung Other_Finding
Behandlung mit Oxaliplatin und Irinotecan Therapeutic
The model has been trained on gold standard labels in GGPONC 2.0 (https://aclanthology.org/2022.lrec-1.389/).
It detects the following 8 entity classes:
- Findings: Diagnosis / Pathology and Other Findings
- Substances: Clinical Drug, Nutrients / Body Substances, External Substances
- Procedures: Therapeutic, Diagnostic
The configuration for training the model is available here: https://github.com/hpi-dhc/ggponc
When using the model, please cite the following publication:
@inproceedings{borchert-etal-2022-ggponc,
title = "{GGPONC} 2.0 - The {G}erman Clinical Guideline Corpus for Oncology: Curation Workflow, Annotation Policy, Baseline {NER} Taggers",
author = "Borchert, Florian and
Lohr, Christina and
Modersohn, Luise and
Witt, Jonas and
Langer, Thomas and
Follmann, Markus and
Gietzelt, Matthias and
Arnrich, Bert and
Hahn, Udo and
Schapranow, Matthieu-P.",
booktitle = "Proceedings of the Thirteenth Language Resources and Evaluation Conference",
month = jun,
year = "2022",
address = "Marseille, France",
publisher = "European Language Resources Association",
pages = "3650--3660"
}
Feature | Description |
---|---|
Name | de_ggponc_medbertde |
Version | 1.0.0 |
spaCy | >=3.4.4,<3.5.0 |
Default Pipeline | transformer , morphologizer , parser , transformer_spancat , spancat |
Components | transformer , morphologizer , parser , transformer_spancat , spancat |
License | The model may be used for non-commercial research activities only, see also the Terms of Use of GGPONC: https://www.leitlinienprogramm-onkologie.de/projekte/ggponc-english |
Author | Florian Borchert |
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Dataset used to train phlobo/de_ggponc_medbertde
Evaluation results
- F1 score (Test set, fine-grained, nested spans)self-reported0.742
- Precision (Test set, fine-grained, nested spans)self-reported0.730
- Recall (Test set, fine-grained, nested spans)self-reported0.753