Generative Adversarial Networks Using Name Entity Recognition Model Using Clinical Health Records
Keywords:
Generative adversarial networks, Deep neural network, word embedding, Name entity recognitionAbstract
Recently, there has been increased attention in the Clinical Named Entity Recognition research area within Medical Records (MR). As much clinical-related information exists in structured and unstructured textual data, Named Entity Recognition technology helps extract different types of patient data. The widespread use of MR has sparked interest in utilizing technology, especially in Biomedical Named Entity Recognition, which faces challenges due to various entities such as medications, genes, diseases, and proteins. Recently, advanced NLP technology has shown outstanding performance through pre-training textual encoders. The encoding of input data is pivotal to the effectiveness of neural sequence labeling models, as they are essential for generating the morphological data. This paper focuses on a variant of the deep neural network model to improve the proposed method. This analysis tackles the challenge of Biomedical Named Entity Recognition by employing Generative adversarial networks that integrate biological data analysis. Numerical sequences are converted into word embedding models. The creation of embeddings based on input is facilitated by pre-trained word embeddings such as GloVe. The model efficiency achieves an improved accuracy of 97.74%.
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