Medical text data records detailed clinical data; named entity recognition is the basis of text information processing and an important part of mining valuable information in medical texts. The named entity recognition technology can accurately identify the information needed in medical texts and help medical staff make clinical decision-making, evidence-based medicine, and epidemic disease monitoring. This paper proposes a hybrid neural network medical text named entity recognition model. First, a coding method based on a fully self-attentive mechanism is proposed. The vector representation of each word is related to the entire sentence through the attention mechanism. It determines the weight distribution by scoring the characters or words in all positions and obtains the position information in the sentence that needs the most attention. The encoding vector at each position is integrated with the context information of full sentence, which solves the ambiguity problem. Second, a multivariate convolutional decoding method is proposed. This method can effectively pay attention to the characteristics of medical text named entity recognition in the decoding process. It uses two-dimensional convolutional decoding to associate the current position word with surrounding words to improve decoding efficiency while extracting features from the logic of the preceding and following words. Using the same number of convolution kernels as the entity category, it can effectively extract effective features from the label dimension. Besides, according to the characteristics of the named entity recognition task, a special mixed loss is designed. The experimental results verify that the proposed method is effective, and it is improved compared with some existing medical text named entity recognition methods.
Technology innovation capability as an endogenous driving force plays an increasingly important role in the low-carbon transformation of new urbanization. This paper's purpose is to delve into the coupling coordination relationship among the three variables, and promote system's and region's synergy development. Based on the coupling coordination degree model, spatial autocorrelation model and obstacle degree model, this paper investigated the coupling coordination of low-carbon development (LCD) quality, technology innovation (TI) capability and new urbanization (NU) level in China from 2009 to 2019. The results indicate: (1) The coupling coordination degree (CCD) of LCD quality, TI capability and NU level in all regions of the country were fluctuating for a long time, and the regions that reach the coordinated development level showed a slow rising trend with obvious regional differences. (2) Three subsystems' CCD showed significant spatial correlation characteristics, and the degree of spatial agglomeration was constantly increasing. (3) The obstacles affecting the systems' synergy mainly reflected in economic and social indexes. In the end, this paper proposed that policy coordination and linkage should be strengthened, emphasizing the integrated development of the three subsystems. It is necessary to formulate development plans in combination with geographic location and resource endowment to enhance the regional driving effect.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.