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Towards Harmonized Data Processing in SMBG

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Precision Medicine Powered by pHealth and Connected Health (ICBHI 2017)

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Abstract

Self-monitoring of blood glucose (SMBG) is the key activity in diabetes management. Patients are required to take measurements and act accordingly, while the physicians use measured data to adjust the therapy. Though the accuracy of individual glucose meters used for SMBG is limited, the main difficulty in interpretation of the recorded data is due to inaccuracy of the records made by patients themselves into the paper diabetic diary. Oftentimes, patients do not record data properly and therefore the data is not reliable for use in determining long-term changes and trends or to use it for further analysis. Therefore, analysis and decision making should rely on the values recorded and stored in glucose memory. The large variety of glucometer models on the market introduce a large problem in using the recorded values since companies which produce and sale glucometers do not necessarily base their data transmission code on accepted standards but they embed custom made code. Data from 37 models of glucometers is transferred into a cloud based platform using previously developed system and available for immediate analysis and for saving into an appropriate health registry in a harmonized structure despite differences in protocols and data structure of different meters. Immediate statistics are given to the physicians upon patient’s checkup. However, general statistical metrics usually do not include metrics on glucose variability, which is one of the most important measurements of glycemic control. We added glycemic variability metrics, including other metrics into tool for data analysis using MATLAB. The output of the analysis can be stored in the system and can be combined with the existing healthcare registries to develop multidimensional analysis for new knowledge discovery. This paper describes the system for acquisition of SMBG data, MATLAB analysis software and the notes on the analysis of the previously discussed data set.

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Correspondence to Sara Zulj .

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Zulj, S., Seketa, G., Magjarevic, R. (2018). Towards Harmonized Data Processing in SMBG. In: Maglaveras, N., Chouvarda, I., de Carvalho, P. (eds) Precision Medicine Powered by pHealth and Connected Health. ICBHI 2017. IFMBE Proceedings, vol 66. Springer, Singapore. https://doi.org/10.1007/978-981-10-7419-6_11

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  • DOI: https://doi.org/10.1007/978-981-10-7419-6_11

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-10-7418-9

  • Online ISBN: 978-981-10-7419-6

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