Open Access
| Issue |
E3S Web Conf.
Volume 185, 2020
2020 International Conference on Energy, Environment and Bioengineering (ICEEB 2020)
|
|
|---|---|---|
| Article Number | 03001 | |
| Number of page(s) | 4 | |
| Section | Medical Biology and Medical Signal Processing | |
| DOI | https://doi.org/10.1051/e3sconf/202018503001 | |
| Published online | 01 September 2020 | |
- B. Song, L.J. Zhang, Y.X. Feng, Research on electronic medical records and related technologies, China Digital Medicine, 2017, vol.12, pp.10–12. [CrossRef] [Google Scholar]
- Y.M. Ding, Y.D. Chen, Refined consultation management based on electronic medical record system, Jilin Medical Journal, 2017, vol.9, pp.1796–1797. [Google Scholar]
- P. Han, Y.Z. Liu, X.Y. Li, Chinese electronic medical record entity recognition research based on deep learning and multi-feature fusion, Journal of Nanjing University (Natural Science), 2019, vol.6, pp.942–951. [Google Scholar]
- M.J. Yang, X.C. Xiong, Construction of Diabetes Knowledge Atlas Based on Reptile Technology and Electronic Medical Records, China Digital Medicine, 2020, vol.2, pp.6–8. [Google Scholar]
- G.Y. He, X.C. Xiao, X.N. Xie, et al., Status and prediction of hypoglycemia in diabetic patients, Chinese Journal of Diabetes, 2019, vol.11, pp.877–880. [Google Scholar]
- W. Qin, M. Gao, Y. Shen, etc., 3-month blood glucose prediction for type 2 diabetes patients based on machine learning algorithms, Chinese Journal of Disease Control & Prevention, 2019, vol.11, pp.1313–1317. [Google Scholar]
- X.H. Wu, Y.P. Zhou, H.H. Xing, etc., Application Research of Machine Learning Classification Algorithm in Diabetes Diagnosis, Computer Knowledge and Technology, 2018, vol.35, pp.177–178+ 195. [Google Scholar]
- M.J. Yang, K.X. Pu, J. Li, Data Preprocessing of Diabetes Electronic Medical Records, Journal of Medical Informatics, 2016, vol.5, pp.59–62 + 84. [Google Scholar]
- X. Liu, C. Qu, S.T. Wang, et al. Analysis of the rules of Chinese medicine treatment of type 2 diabetes based on data mining, Chinese Archives of Traditional Chinese Medicine, 2019, vol.2, pp.1–17. [Google Scholar]
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