Chinese diabetes datasets for data-driven machine learning

Qinpei Zhao, Jinhao Zhu, Xuan Shen, Chuwen Lin, Yinjia Zhang, Yuxiang Liang, Baige Cao, Jiangfeng Li*, Xiang Liu, Weixiong Rao, Congrong Wang

*Corresponding author for this work

Research output: Contribution to journalData ArticleScientificpeer-review

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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.

Original languageEnglish
Article number35
JournalScientific Data
Volume10
Issue number1
DOIs
Publication statusPublished - 19 Jan 2023
MoE publication typeA1 Journal article-refereed

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