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Volume 20 Supplement 3

Health Information Processing

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.

Guangzhou, China22-24 November 2019

Conference website

Edited by Hongfei Lin, Lei Liu and Qingcai Chen

  1. It is of utmost importance to investigate novel therapies for cancer, as it is a major cause of death. In recent years, immunotherapies, especially those against immune checkpoints, have been developed and bro...

    Authors: Yuyu Zheng, Xiangyu Meng, Pierre Zweigenbaum, Lingling Chen and Jingbo Xia

    Citation: BMC Medical Informatics and Decision Making 2020 20(Suppl 3):133

    Content type: Research

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  2. Cardiogenic stroke has increasing morbidity in China and brought economic burden to patient families. In cardiogenic stroke diagnosis, echocardiograph examination is one of the most important examinations. Son...

    Authors: Lu Qin, Xiaowei Xu, Lingling Ding, Zixiao Li and Jiao Li

    Citation: BMC Medical Informatics and Decision Making 2020 20(Suppl 3):126

    Content type: Research

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  3. In the few studies of clinical experience available, cigarette smoking may be associated with ischemic heart disease and acute coronary events, which can be reflected in the electrocardiogram (ECG). However, t...

    Authors: Kuo-Kun Tseng, Jiaqian Li, Yih-Jing Tang, Ching Wen Yang and Fang-Ying Lin

    Citation: BMC Medical Informatics and Decision Making 2020 20(Suppl 3):127

    Content type: Research

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  4. Deep learning based on segmentation models have been gradually applied in biomedical images and achieved state-of-the-art performance for 3D biomedical segmentation. However, most of existing biomedical segmen...

    Authors: Xibin Jia, Yunfeng Liu, Zhenghan Yang and Dawei Yang

    Citation: BMC Medical Informatics and Decision Making 2020 20(Suppl 3):119

    Content type: Research

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  5. The interpretability of results predicted by the machine learning models is vital, especially in the critical fields like healthcare. With the increasingly adoption of electronic healthcare records (EHR) by th...

    Authors: Peipei Chen, Wei Dong, Jinliang Wang, Xudong Lu, Uzay Kaymak and Zhengxing Huang

    Citation: BMC Medical Informatics and Decision Making 2020 20(Suppl 3):131

    Content type: Research

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  6. Although clinical guidelines provide the best practice for medical activities, there are some limitations in using clinical guidelines to assistant decision-making in practical application, such as long update...

    Authors: Haifeng Xu, Jianfei Pang, Xi Yang, Mei Li and Dongsheng Zhao

    Citation: BMC Medical Informatics and Decision Making 2020 20(Suppl 3):120

    Content type: Research

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  7. Hepatocellular carcinoma is one of the most general malignant neoplasms in adults with high mortality. Mining relative medical knowledge from rapidly growing text data and integrating it with other existing bi...

    Authors: Nan Li, Zhihao Yang, Ling Luo, Lei Wang, Yin Zhang, Hongfei Lin and Jian Wang

    Citation: BMC Medical Informatics and Decision Making 2020 20(Suppl 3):135

    Content type: Research

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  8. Blood cultures are often performed to detect patients who has a serious illness without infections and patients with bloodstream infections. Early positive blood culture prediction is important, as bloodstream...

    Authors: Ming Cheng, Xiaolei Zhao, Xianfei Ding, Jianbo Gao, Shufeng Xiong and Yafeng Ren

    Citation: BMC Medical Informatics and Decision Making 2020 20(Suppl 3):121

    Content type: Research

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  9. With the rapid development of sequencing technologies, collecting diverse types of cancer omics data become more cost-effective. Many computational methods attempted to represent and fuse multiple omics into a...

    Authors: Kaiwen Tan, Weixian Huang, Jinlong Hu and Shoubin Dong

    Citation: BMC Medical Informatics and Decision Making 2020 20(Suppl 3):129

    Content type: Research

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  10. Bladder cancer (BC) is regarded as one of the most fatal cancer around the world. Nevertheless, there still lack of sufficient markers to predict the prognosis of BC patients. Herein, we aim to establish a pro...

    Authors: Xiaolong Zhang, Meng Zhang, Xuanping Zhang, Xiaoyan Zhu and Jiayin Wang

    Citation: BMC Medical Informatics and Decision Making 2020 20(Suppl 3):136

    Content type: Research

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  11. The increasing global cancer incidence corresponds to serious health impact in countries worldwide. Knowledge-powered health system in different languages would enhance clinicians’ healthcare practice, patient...

    Authors: Hetong Ma, Feihong Yang, Jiansong Ren, Ni Li, Min Dai, Xuwen Wang, An Fang, Jiao Li, Qing Qian and Jie He

    Citation: BMC Medical Informatics and Decision Making 2020 20(Suppl 3):122

    Content type: Research

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  12. Circular RNAs (circRNAs) are those RNA molecules that lack the poly (A) tails, which present the closed-loop structure. Recent studies emphasized that some circRNAs imply different functions from canonical tra...

    Authors: Yidan Wang, Xuanping Zhang, Tao Wang, Jinchun Xing, Zhun Wu, Wei Li and Jiayin Wang

    Citation: BMC Medical Informatics and Decision Making 2020 20(Suppl 3):137

    Content type: Research

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  13. Electronic medical records contain a variety of valuable medical information for patients. So, when we are able to recognize and extract risk factors for disease from EMRs of patients with cardiovascular disea...

    Authors: Zhichang Zhang, Yanlong Qiu, Xiaoli Yang and Minyu Zhang

    Citation: BMC Medical Informatics and Decision Making 2020 20(Suppl 3):123

    Content type: Research

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  14. Reinforcement learning (RL) provides a promising technique to solve complex sequential decision making problems in healthcare domains. Recent years have seen a great progress of applying RL in addressing decis...

    Authors: Chao Yu, Guoqi Ren and Yinzhao Dong

    Citation: BMC Medical Informatics and Decision Making 2020 20(Suppl 3):124

    Content type: Research

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  15. To provide satisfying answers, medical QA system has to understand the intentions of the users’ questions precisely. For medical intent classification, it requires high-quality datasets to train a deep-learnin...

    Authors: Nan Chen, Xiangdong Su, Tongyang Liu, Qizhi Hao and Ming Wei

    Citation: BMC Medical Informatics and Decision Making 2020 20(Suppl 3):125

    Content type: Research

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  16. Nowadays, the latent power of technology, which can offer innovative resolutions to disease diagnosis, has awakened high-level anticipation in the community of patients as well as professionals. An easy-to-use...

    Authors: Fan Guo, Weiqing Li, Xin Zhao, Junfeng Qiu and Yuxiang Mai

    Citation: BMC Medical Informatics and Decision Making 2020 20(Suppl 3):128

    Content type: Research

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  17. With China experiencing unprecedented economic development and social change over the past three decades, Chinese policy makers and health care professionals have come to view mental health as an important out...

    Authors: Wenyan Tan, Haicheng Lin, Baoxin Lei, Aihua Ou, Zehui He, Ning Yang, Fujun Jia, Heng Weng and Tianyong Hao

    Citation: BMC Medical Informatics and Decision Making 2020 20(Suppl 3):132

    Content type: Research

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