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2,767 results on '"Wang, Xiaohua"'

3. Chinese Clinical Named Entity Recognition in Electronic Medical Records: Development of a Lattice Long Short-Term Memory Model With Contextualized Character Representations

Author

Li, Yongbin, Wang, Xiaohua, Hui, Linhu, Zou, Liping, Li, Hongjin, Xu, Luo, and Liu, Weihai

Subjects
Computer applications to medicine. Medical informatics, R858-859.7
Abstract

BackgroundClinical named entity recognition (CNER), whose goal is to automatically identify clinical entities in electronic medical records (EMRs), is an important research direction of clinical text data mining and information extraction. The promotion of CNER can provide support for clinical decision making and medical knowledge base construction, which could then improve overall medical quality. Compared with English CNER, and due to the complexity of Chinese word segmentation and grammar, Chinese CNER was implemented later and is more challenging. ObjectiveWith the development of distributed representation and deep learning, a series of models have been applied in Chinese CNER. Different from the English version, Chinese CNER is mainly divided into character-based and word-based methods that cannot make comprehensive use of EMR information and cannot solve the problem of ambiguity in word representation. MethodsIn this paper, we propose a lattice long short-term memory (LSTM) model combined with a variant contextualized character representation and a conditional random field (CRF) layer for Chinese CNER: the Embeddings from Language Models (ELMo)-lattice-LSTM-CRF model. The lattice LSTM model can effectively utilize the information from characters and words in Chinese EMRs; in addition, the variant ELMo model uses Chinese characters as input instead of the character-encoding layer of the ELMo model, so as to learn domain-specific contextualized character embeddings. ResultsWe evaluated our method using two Chinese CNER datasets from the China Conference on Knowledge Graph and Semantic Computing (CCKS): the CCKS-2017 CNER dataset and the CCKS-2019 CNER dataset. We obtained F1 scores of 90.13% and 85.02% on the test sets of these two datasets, respectively. ConclusionsOur results show that our proposed method is effective in Chinese CNER. In addition, the results of our experiments show that variant contextualized character representations can significantly improve the performance of the model.

Published
2020
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4. When Public Health Research Meets Social Media: Knowledge Mapping From 2000 to 2018

Author

Zhang, Yan, Cao, Bolin, Wang, Yifan, Peng, Tai-Quan, and Wang, Xiaohua

Subjects
Computer applications to medicine. Medical informatics, R858-859.7, Public aspects of medicine, RA1-1270
Abstract

BackgroundSocial media has substantially changed how people confront health issues. However, a comprehensive understanding of how social media has altered the foci and methods in public health research remains lacking. ObjectiveThis study aims to examine research themes, the role of social media, and research methods in social media–based public health research published from 2000 to 2018. MethodsA dataset of 3419 valid studies was developed by searching a list of relevant keywords in the Web of Science and PubMed databases. In addition, this study employs an unsupervised text-mining technique and topic modeling to extract research themes of the published studies. Moreover, the role of social media and research methods adopted in those studies were analyzed. ResultsThis study identifies 25 research themes, covering different diseases, various population groups, physical and mental health, and other significant issues. Social media assumes two major roles in public health research: produce substantial research interest for public health research and furnish a research context for public health research. Social media provides substantial research interest for public health research when used for health intervention, human-computer interaction, as a platform of social influence, and for disease surveillance, risk assessment, or prevention. Social media acts as a research context for public health research when it is mere reference, used as a platform to recruit participants, and as a platform for data collection. While both qualitative and quantitative methods are frequently used in this emerging area, cutting edge computational methods play a marginal role. ConclusionsSocial media enables scholars to study new phenomena and propose new research questions in public health research. Meanwhile, the methodological potential of social media in public health research needs to be further explored.

Published
2020
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32. Efficacy and safety of vunakizumab in moderate-to-severe chronic plaque psoriasis: A randomized, double-blind, placebo-controlled phase 3 trial

Author

Yan, Kexiang, Li, Fuqiu, Bi, Xiaodong, Han, Ling, Zhang, Zhenghua, Chen, Rixin, Li, Yuye, Zhang, Litao, Wang, Xiaohua, Li, Linfeng, Lu, Jianyun, Xu, Ai'e, Yang, Sen, Lu, Yan, Sun, Jianfang, Li, Zhiming, Zhu, Xiaohong, Jiang, Meiying, Zhang, Siping, Wang, Wenqing, Li, Yanling, Meng, Zudong, Li, Hongyi, Mou, Kuanhou, Han, Xiuping, Li, Shanshan, Chen, Aijun, Li, Xin, Liu, Donghua, Zhang, Chunlei, Ji, Chao, Wang, Yu, Cheng, Hao, Cui, Xiaojing, Yao, Xiaoyan, Bai, Xiaoyan, Dong, Guangchao, and Xu, Jinhua

Published
2025
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