TY - JOUR
T1 - Chinese diabetes datasets for data-driven machine learning
AU - Zhao, Qinpei
AU - Zhu, Jinhao
AU - Shen, Xuan
AU - Lin, Chuwen
AU - Zhang, Yinjia
AU - Liang, Yuxiang
AU - Cao, Baige
AU - Li, Jiangfeng
AU - Liu, Xiang
AU - Rao, Weixiong
AU - Wang, Congrong
N1 - 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).
PY - 2023/1/19
Y1 - 2023/1/19
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85146485715&partnerID=8YFLogxK
U2 - 10.1038/s41597-023-01940-7
DO - 10.1038/s41597-023-01940-7
M3 - Data Article
C2 - 36653358
AN - SCOPUS:85146485715
SN - 2052-4463
VL - 10
JO - Scientific Data
JF - Scientific Data
IS - 1
M1 - 35
ER -
