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Volume 19 Supplement 2

Proceedings from the 4th China Health Information Processing Conference (CHIP 2018)

Research

Publication of this supplement has not been supported by sponsorship. Information about the source of funding for publication charges can be found in the individual articles. The articles have undergone the journal's standard peer review process for supplements. The Supplement Editors declare that they have no competing interests.

Shenzhen, China1-2 December 2018

Conference website

Edited by Lei Liu and Hongfei Lin

  1. Content type: Research

    In recent years, the increasing incidence and prevalence of stroke has brought a heavy economic burden on families and society in China. The Ministry of Health of the Peoples’ Republic of China initiated the n...

    Authors: Xuemeng Li, Jianfei Pang, Mei Li and Dongsheng Zhao

    Citation: BMC Medical Informatics and Decision Making 2019 19(Suppl 2):67

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  2. Content type: Research

    Major adverse cardiac event (MACE) prediction plays a key role in providing efficient and effective treatment strategies for patients with acute coronary syndrome (ACS) during their hospitalizations. Existing ...

    Authors: Danqing Hu, Wei Dong, Xudong Lu, Huilong Duan, Kunlun He and Zhengxing Huang

    Citation: BMC Medical Informatics and Decision Making 2019 19(Suppl 2):61

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  3. Content type: Research

    Reinforcement learning (RL) provides a promising technique to solve complex sequential decision making problems in health care domains. However, existing studies simply apply naive RL algorithms in discovering...

    Authors: Chao Yu, Yinzhao Dong, Jiming Liu and Guoqi Ren

    Citation: BMC Medical Informatics and Decision Making 2019 19(Suppl 2):60

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  4. Content type: Research

    Medical event detection in narrative clinical notes of electronic health records (EHRs) is a task designed for reading text and extracting information. Most of the previous work of medical event detection trea...

    Authors: Xuesi Zhou, Haoqi Xiong, Sihan Zeng, Xiangling Fu and Ji Wu

    Citation: BMC Medical Informatics and Decision Making 2019 19(Suppl 2):54

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  5. Content type: Research

    Social media plays a more and more important role in the research of health and healthcare due to the fast development of internet communication and information exchange. This paper conducts a bibliometric ana...

    Authors: Xieling Chen, Yonghui Lun, Jun Yan, Tianyong Hao and Heng Weng

    Citation: BMC Medical Informatics and Decision Making 2019 19(Suppl 2):50

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  6. Content type: Research

    Extracting relations between bio-entities from biomedical literature is often a challenging task and also an essential step towards biomedical knowledge expansion. The BioCreative community has organized a sha...

    Authors: Suwen Liu, Yifan Shao, Longhua Qian and Guodong Zhou

    Citation: BMC Medical Informatics and Decision Making 2019 19(Suppl 2):63

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  7. Content type: Research

    Electronic Medical Records(EMRs) contain much medical information about patients. Medical named entity extracting from EMRs can provide value information to support doctors’ decision making. The research on in...

    Authors: Yan Gao, Lei Gu, Yefeng Wang, Yandong Wang and Feng Yang

    Citation: BMC Medical Informatics and Decision Making 2019 19(Suppl 2):56

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  8. Content type: Research

    The traditional Chinese Medicine Language System (TCMLS) is a large-scale terminology system, developed from 2002 on by the Institute of Information of Traditional Chinese Medicine (IITCM). Until now, more tha...

    Authors: Hai Long, Yan Zhu, Lirong Jia, Bo Gao, Jing Liu, Lihong Liu and Heinrich Herre

    Citation: BMC Medical Informatics and Decision Making 2019 19(Suppl 2):53

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  9. Content type: Research

    Medical and clinical question answering (QA) is highly concerned by researchers recently. Though there are remarkable advances in this field, the development in Chinese medical domain is relatively backward. I...

    Authors: Junqing He, Mingming Fu and Manshu Tu

    Citation: BMC Medical Informatics and Decision Making 2019 19(Suppl 2):52

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  10. Content type: Research

    The Named Entity Recognition (NER) task as a key step in the extraction of health information, has encountered many challenges in Chinese Electronic Medical Records (EMRs). Firstly, the casual use of Chinese a...

    Authors: Xiaoling Cai, Shoubin Dong and Jinlong Hu

    Citation: BMC Medical Informatics and Decision Making 2019 19(Suppl 2):65

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  11. Content type: Research

    Reinforcement learning (RL) provides a promising technique to solve complex sequential decision making problems in health care domains. To ensure such applications, an explicit reward function encoding domain ...

    Authors: Chao Yu, Jiming Liu and Hongyi Zhao

    Citation: BMC Medical Informatics and Decision Making 2019 19(Suppl 2):57

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  12. Content type: Research

    Drug development is an expensive and time-consuming process. Literature-based discovery has played a critical role in drug development and may be a supplementary method to help scientists speed up the discover...

    Authors: Di Zhao, Jian Wang, Shengtian Sang, Hongfei Lin, Jiabin Wen and Chunmei Yang

    Citation: BMC Medical Informatics and Decision Making 2019 19(Suppl 2):59

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  13. Content type: Research

    Disease prediction based on Electronic Health Records (EHR) has become one hot research topic in biomedical community. Existing work mainly focuses on the prediction of one target disease, and little work is p...

    Authors: Yafeng Ren, Hao Fei, Xiaohui Liang, Donghong Ji and Ming Cheng

    Citation: BMC Medical Informatics and Decision Making 2019 19(Suppl 2):51

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  14. Content type: Research

    Learning distributional representation of clinical concepts (e.g., diseases, drugs, and labs) is an important research area of deep learning in the medical domain. However, many existing relevant methods do no...

    Authors: Yang Xiang, Jun Xu, Yuqi Si, Zhiheng Li, Laila Rasmy, Yujia Zhou, Firat Tiryaki, Fang Li, Yaoyun Zhang, Yonghui Wu, Xiaoqian Jiang, Wenjin Jim Zheng, Degui Zhi, Cui Tao and Hua Xu

    Citation: BMC Medical Informatics and Decision Making 2019 19(Suppl 2):58

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  15. Content type: Research

    With the rapid spread of electronic medical records and the arrival of medical big data era, the application of natural language processing technology in biomedicine has become a hot research topic.

    Authors: Bin Ji, Rui Liu, Shasha Li, Jie Yu, Qingbo Wu, Yusong Tan and Jiaju Wu

    Citation: BMC Medical Informatics and Decision Making 2019 19(Suppl 2):64

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  16. Content type: Research

    Gender information frequently exists in the eligibility criteria of clinical trial text as essential information for participant population recruitment. Particularly, current eligibility criteria text contains...

    Authors: Boyu Chen, Hao Jin, Zhiwen Yang, Yingying Qu, Heng Weng and Tianyong Hao

    Citation: BMC Medical Informatics and Decision Making 2019 19(Suppl 2):62

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  17. Content type: Research

    Electronic medical records (EMRs) contain a variety of valuable medical concepts and relations. The ability to recognize relations between medical concepts described in EMRs enables the automatic processing of...

    Authors: Zhichang Zhang, Tong Zhou, Yu Zhang and Yali Pang

    Citation: BMC Medical Informatics and Decision Making 2019 19(Suppl 2):55

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  18. Content type: Research

    Chinese word segmentation (CWS) and part-of-speech (POS) tagging are two fundamental tasks of Chinese text processing. They are usually preliminary steps for lots of Chinese natural language processing (NLP) t...

    Authors: Ying Xiong, Zhongmin Wang, Dehuan Jiang, Xiaolong Wang, Qingcai Chen, Hua Xu, Jun Yan and Buzhou Tang

    Citation: BMC Medical Informatics and Decision Making 2019 19(Suppl 2):66

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  19. Content type: Research

    Diabetes has become one of the hot topics in life science researches. To support the analytical procedures, researchers and analysts expend a mass of labor cost to collect experimental data, which is also erro...

    Authors: Fan Gong, Yilei Chen, Haofen Wang and Hao Lu

    Citation: BMC Medical Informatics and Decision Making 2019 19(Suppl 2):49

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