This is a model for named entity recognition of Japanese medical documents.


	
		
	
	
		How to use
	


Download the following five files and put into the same folder.


id_to_tags.pkl
key_attr.pkl
NER_medNLP.py
predict.py
text.txt (This is an input file which should be predicted, which could be changed.)


You can use this model by running predict.py.


	
		
	
	
		Input Example
	



	
		
	
	
		Output Example
	



	
		
	
	
		Publication
	


Tomohiro Nishiyama, Aki Ando, Mihiro Nishidani, Shuntaro Yada, Shoko Wakamiya, Eiji Aramaki: NAISTSOC at the NTCIR-16 Real-MedNLP Task, In Proceedings of the 16th NTCIR Conference on Evaluation of Information Access Technologies (NTCIR-16), pp. 330-333, 2022



MedNER-CR-JA is a model for named entity recognition (NER) of Japanese medical documents. It is designed to identify and classify specific entities such as diseases, symptoms, treatments, and anatomical terms in the text. The model takes in a Japanese medical document as input and outputs the recognized entity mentions along with their corresponding entity labels. It can be used by running the provided predict.py script with the necessary files in the same folder. The model has been evaluated in the NTCIR-16 Real-MedNLP Task and achieved competitive results.

Sociocom

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MedNER-CR-JA

MedNERN-CR-JA

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