Fingerprint Feature Extraction of Electronic Medical Records Based on Few-Shot Named Entity Recognition Technology
With the promulgation and implementation of the"Personal Information Protection Law of the People's Republic of China""Data Security Law of the People's Republic of China"and other relevant laws and regulations,electronic medical record data protection has attracted much attention.Fast and efficient identification of electronic medical records is the first link of data protection and an important research topic in the field of data security.This paper proposed an electronic medical record fingerprint feature extraction method based on few-shot named entity recognition technology.First,the encoder was trained through a public dataset to obtain a broad text feature space.Subsequently,the encoder was fine-tuned using the electronic medical record dataset,and the entity type label was characterized by a prototype network.Finally,the fingerprint feature of"entity type+entity set"was obtained by extracting the electronic medical record feature.The experimental results show that the method has excellent performance on the I2B2 dataset,surpassing other models and effectively improving the privacy protection ability of electronic medical record dataset.
data securityelectronic medical recordscomparative learningnamed entity recognitionfew-shot learning
万方数据
维普
