Blood glucose forecasting from temporal and static information in children with T1D.
| Autor: | Marx A; Department of Computer Science, ETH Zurich, Zurich, Switzerland., Di Stefano F; Department of Computer Science, ETH Zurich, Zurich, Switzerland., Leutheuser H; Department of Computer Science, ETH Zurich, Zurich, Switzerland., Chin-Cheong K; Department of Computer Science, ETH Zurich, Zurich, Switzerland., Pfister M; Pediatric Pharmacology and Pharmacometrics, University Children's Hospital Basel, Basel, Switzerland.; Department of Clinical Research, University Hospital Basel, Basel, Switzerland., Burckhardt MA; Department of Clinical Research, University Hospital Basel, Basel, Switzerland.; Pediatric Endocrinolgy and Diabetology, University Children's Hospital Basel, Basel, Switzerland., Bachmann S; Department of Clinical Research, University Hospital Basel, Basel, Switzerland.; Pediatric Endocrinolgy and Diabetology, University Children's Hospital Basel, Basel, Switzerland., Vogt JE; Department of Computer Science, ETH Zurich, Zurich, Switzerland. |
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| Jazyk: | angličtina |
| Zdroj: | Frontiers in pediatrics [Front Pediatr] 2023 Dec 14; Vol. 11, pp. 1296904. Date of Electronic Publication: 2023 Dec 14 (Print Publication: 2023). |
| DOI: | 10.3389/fped.2023.1296904 |
| Abstrakt: | Background: The overarching goal of blood glucose forecasting is to assist individuals with type 1 diabetes (T1D) in avoiding hyper- or hypoglycemic conditions. While deep learning approaches have shown promising results for blood glucose forecasting in adults with T1D, it is not known if these results generalize to children. Possible reasons are physical activity (PA), which is often unplanned in children, as well as age and development of a child, which both have an effect on the blood glucose level. Materials and Methods: In this study, we collected time series measurements of glucose levels, carbohydrate intake, insulin-dosing and physical activity from children with T1D for one week in an ethics approved prospective observational study, which included daily physical activities. We investigate the performance of state-of-the-art deep learning methods for adult data-(dilated) recurrent neural networks and a transformer-on our dataset for short-term ( 30 min) and long-term ( 2 h) prediction. We propose to integrate static patient characteristics, such as age, gender, BMI, and percentage of basal insulin, to account for the heterogeneity of our study group. Results: Integrating static patient characteristics (SPC) proves beneficial, especially for short-term prediction. LSTMs and GRUs with SPC perform best for a prediction horizon of 30 min (RMSE of 1.66 mmol/l), a vanilla RNN with SPC performs best across different prediction horizons, while the performance significantly decays for long-term prediction. For prediction during the night, the best method improves to an RMSE of 1.50 mmol/l. Overall, the results for our baselines and RNN models indicate that blood glucose forecasting for children conducting regular physical activity is more challenging than for previously studied adult data. Conclusion: We find that integrating static data improves the performance of deep-learning architectures for blood glucose forecasting of children with T1D and achieves promising results for short-term prediction. Despite these improvements, additional clinical studies are warranted to extend forecasting to longer-term prediction horizons. Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. (© 2023 Marx, Di Stefano, Leutheuser, Chin-Cheong, Pfister, Burckhardt, Bachmann and Vogt.) |
| Databáze: | MEDLINE |
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