Chinese diabetes datasets for data-driven machine learning
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Authors
Zhao, Qinpei
Zhu, Jinhao
Shen, Xuan
Lin, Chuwen
Zhang, Yinjia
Liang, Yuxiang
Cao, Baige
Li, Jiangfeng
Liu, Xiang
Rao, Weixiong
Date
2023-01-19
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Department of Computer Science
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en
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Scientific Data, Volume 10, issue 1
Abstract
Data of the diabetes mellitus patients is essential in the study of diabetes management, especially when employing the data-driven machine learning methods into the management. To promote and facilitate the research in diabetes management, we have developed the ShanghaiT1DM and ShanghaiT2DM Datasets and made them publicly available for research purposes. This paper describes the datasets, which was acquired on Type 1 (n = 12) and Type 2 (n = 100) diabetic patients in Shanghai, China. The acquisition has been made in real-life conditions. The datasets contain the clinical characteristics, laboratory measurements and medications of the patients. Moreover, the continuous glucose monitoring readings with 3 to 14 days as a period together with the daily dietary information are also provided. The datasets can contribute to the development of data-driven algorithms/models and diabetes monitoring/managing technologies.Description
Funding Information: This work was supported by the National Natural Science Foundation of China (Grant No. 61972286, 82070913), the Shanghai Municipal Science and Technology Major Project (2021SHZDZX0100), the Natural Science Foundation of Shanghai, China (Grant No. 20ZR1460500, 22511104300), the Shanghai Science and Technology Development Funds (Grant No. 20ZR1446000, 22410713200), the Fundamental Research Funds for the Central Universities and the Research fund from Shanghai Fourth People’s Hospital (sykyqd01801, SY-XKZT-2021-1001). Finally, thanks Ms. Xiongbaixue Yan for her previous efforts on the management of the project. Publisher Copyright: © 2023, The Author(s).
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Zhao, Q, Zhu, J, Shen, X, Lin, C, Zhang, Y, Liang, Y, Cao, B, Li, J, Liu, X, Rao, W & Wang, C 2023, ' Chinese diabetes datasets for data-driven machine learning ', Scientific Data, vol. 10, no. 1, 35 . https://doi.org/10.1038/s41597-023-01940-7