Abstract
The daily self-management of type 1 diabetes (T1D) has benefitted from the advancements in real-time continuous glucose monitoring and hybrid closed-loop insulin delivery. These technologies comprise, in parallel, significant sources of data providing insight into daily glucose control and insulin treatment. The concurrent real-time continuous monitoring of vital signs, 24/7, complements the exploitable information for one individual. In this study, we investigate whether respiratory, hemodynamic, and body temperature vital signs correlate linearly with the subcutaneous glucose concentration in T1D, and improve its short-term, up to 60-min ahead, prediction as compared to a univariate model. To verify our research hypothesis, (i) we approximate the prediction of glucose concentration via a long short-term memory (LSTM) function of the recent 30-min history of glucose and those vital signs with a Pearson’s r > 0.5, and (ii) contrast its performance with that of the univariate model. LSTM has been trained and tested individually, using a dataset with 22 T1D people monitored for 2 or 4 weeks. Our analysis identified that: (i) subcutaneous glucose concentration is linearly correlated principally with heart rate and systolic blood pressure, and (ii) the value of the vital signs lies in the improvement of the predictions in hypoglycaemia as the prediction horizon (PH) increases, where we observed a substantial reduction of erroneous predictions from 19% to 7% for a PH of 60 min.
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Acknowledgement
This research has been co‐financed by the European Regional Development Fund of the European Union and Greek national funds through the Operational Program Competitiveness, Entrepreneurship and Innovation, under the call RESEARCH – CREATE – INNOVATE (project code: T1EDK-03990).
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Katsarou, D.N., Georga, E.I., Christou, M., Tigas, S., Papaloukas, C., Fotiadis, D.I. (2023). An Exploratory Study of the Value of Vital Signs on the Short-Term Prediction of Subcutaneous Glucose Concentration in Type 1 Diabetes – The GlucoseML Study. In: Tsanas, A., Triantafyllidis, A. (eds) Pervasive Computing Technologies for Healthcare. PH 2022. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 488. Springer, Cham. https://doi.org/10.1007/978-3-031-34586-9_30
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DOI: https://doi.org/10.1007/978-3-031-34586-9_30
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