Multilingual Medical Text Processing Solution
Three technical strategies for dealing with non-English texts:
- Specialized model selection::
- Chinese Clinical Text UseOpenMed-NER-ZH-MedBaserange
- French-language documentation processing optionsOpenMed-NER-FR-BioClin
- Support for German/Japanese/SpanishHugging Face Specialized Model Library
- Mixed processing technology::
- First, use the langdetect library to detect the language of the text.
- Automatic routing to the corresponding language model
- Harmonized output to English standard terminology (e.g., UMLS codes)
- Field habilitation: for the absence of a target language model:
from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("OpenMed/OpenMed-NER-MultiLang-434M") model = AutoModelForTokenClassification.from_pretrained("...") # 用目标语言数据继续训练500步
The actual test shows that the recognition F1 of Chinese "insulin" is only 0.62 when using the English model directly, and it is improved to 0.89 after switching to ZH-MedBase.For mixed text such as "patient taking insulin twice a day", it is recommended to process it by language segmentation first. For mixed text such as "patient taking insulin twice a day", it is recommended to perform language segmentation first.
This answer comes from the articleOpenMed: an open source platform for free AI models in healthcareThe
May not be reproduced without permission:AI productivity tools " How to prevent recognition bias in OpenMed models when processing multilingual medical texts?
English 
简体中文 
日本語 
Deutsch 
Português do Brasil