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Database for storing and accessing diabetes related data in a standardized way

文献类型： 会议论文

第一作者： Máté Siket

作者： Máté Siket 1 ; András Nándor Kis 2 ; Lehel Dénes-Fazakas 3 ; Gy?rgy Eigner 4 ; Levente Kovács 4 ;

作者机构： 1.Physiological Controls Research Center, óbuda University|Institute for Computer Science and Control, Kende utca 13-17, Budapest, Hungary|Applied Informatics and Applied Mathematics Doctoral School, óbuda University, H-1034, Budapest, Bécsi street 96/B

2.Physiological Controls Research Center, óbuda University

3.Physiological Controls Research Center, óbuda University|Biomatics and Applied Artificial Intelligence Institute, John von Neumann Faculty of Informatics, óbuda University|Applied Informatics and Applied Mathematics Doctoral School, óbuda University, H-1034, Budapest, Bécsi street 96/B

4.Physiological Controls Research Center, óbuda University|Biomatics and Applied Artificial Intelligence Institute, John von Neumann Faculty of Informatics, óbuda University

关键词： Computer languages;Machine learning algorithms;Databases;Source coding;Pipelines;Transformers;Real-time systems

会议名称： IEEE International Symposium on Computational Intelligence and Informatics

主办单位：

页码： 285-290

摘要： Real-world data has a major importance in diabetes related research, especially considering the widespread applications of machine learning algorithms. There are several existing datasets of real-world data in the literature; however, they all have their specific formats, applied devices and file structures. The different charachteristics make it cumbersome to use multiple datasets for research purposes. We developed a pipeline for efficiently storing and accessing diabetes related data in a standardized way. We defined a standardized JSON format for the records and stored them in a MongoDB database. The pipeline is capable of uploading data from various sources by implementing a corresponding transformer script; also it is possible to extend it with algorithms which are called automatically for new entries in the database. The source code for constructing the pipeline is given in https://github.com/NeuroDiab/CINTI2023Diabdatabase.

分类号： tp3

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