Swarm hybrid optimization for a piecewise model fitting applied to a glucose model

[EN] Purpose ¿ The purpose of this paper is to study insulin pump therapy and accurate monitoring of glucose levels in diabetic patients, which are current research trends in diabetology. Both problems have a wide margin for improvement and promising applications in the control of parameters and levels involved. Design/methodology/approach ¿ The authors have registered data for the levels of glucose in diabetic patients throughout a day with a temporal resolution of 5 minutes, the amount and time of insulin administered and time of ingestion. The estimated quantity of carbohydrates is also monitored. A mathematical model for Type 1 patients was fitted piecewise to these data and the evolution of the parameters was analyzed. Findings ¿ They have found that the parameters for the model change abruptly throughout a day for the same patient, but this set of parameters account with precision for the evolution of the glucose levels in the test patients. This fitting technique could be used to personalize treatments for specific patients and predict the glucose-level variations in terms of hours or even shorter periods of time. This way more effective insulin pump therapies could be developed. Originality/value ¿ The proposed model could allow for the development of improved schedules on insulin pump therapiesAcedo Rodríguez, L.; Botella, M.; Cortés, J.; Hidalgo, J.; Maqueda, E.; Villanueva Micó, RJ. (2018). Swarm hybrid optimization for a piecewise model fitting applied to a glucose model. Journal of Systems and Information Technology. 20(4):9618-9627. https://doi.org/10.1108/JSIT-10-2017-0103S9618962720

A computational technique to predict the level of glucose of a diabetic patient with uncertainty in the short term

[EN] On advanced stages of the disease, diabetic patients have to inject insulin doses to maintain blood glucose levels inside of a healthy range. The decision of how much insulin is injected implies somehow to predict the level of glucose they will have after a certain time. Due to the sudden changes in the glucose levels, their estimation is a very difficult task. If we were able to give reliable estimations in advance, it would facilitate the process of taking therapeutic decisions to control the disease and improve the health of the patient. In this work, we present a technique to estimate the glucose level of a diabetic patient, capturing the measurement errors produced by continuous glucose monitoring systems (CGMSs), smart devices that measure glucose levels. To do that, we will use a model of glucose dynamics and we calibrate it with the aim to capture the glucose level data of the patient in a time interval of 30 minutes and the uncertainty given by the glucose measurement. Then, we use the calibrated parameters to predict the levels of glucose over the next 15 minutes. Repeating this procedure every 15 minutes, we are able to give short¿term accurate predictions.This work has been partially supported by the Spanish Ministerio de Economía y Competitividad under grant MTM2017-89664-P and RTI2018-095180-B-I00 and by Fundación Eugenio Rodriguez Pascual 2019 -GLENO ProjectBurgos Simon, C.; Cervigón, C.; Hidalgo, J.; Villanueva Micó, RJ. (2019). A computational technique to predict the level of glucose of a diabetic patient with uncertainty in the short term. Computational and Mathematical Methods. 2(2):1-11. https://doi.org/10.1002/cmm4.1064S11122(2004). Third-Party Reimbursement for Diabetes Care, Self-Management Education, and Supplies. Diabetes Care, 28(Supplement 1), S62-S63. doi:10.2337/diacare.28.suppl_1.s62Bloomgarden, Z. T. (2004). Consequences of Diabetes: Cardiovascular disease. Diabetes Care, 27(7), 1825-1831. doi:10.2337/diacare.27.7.1825BrownA.Time‐in‐range: what's an achievable goal with diabetes?2017.https://diatribe.org/time-range-whats-achievable-goal-diabetesFonseca, V. A., Grunberger, G., Anhalt, H., Bailey, T. S., Blevins, T., … Garg, S. K. (2016). CONTINUOUS GLUCOSE MONITORING: A CONSENSUS CONFERENCE OF THE AMERICAN ASSOCIATION OF CLINICAL ENDOCRINOLOGISTS AND AMERICAN COLLEGE OF ENDOCRINOLOGY. Endocrine Practice, 22(8), 1008-1021. doi:10.4158/ep161392.csChristiansen, M. P., Klaff, L. J., Brazg, R., Chang, A. R., Levy, C. J., Lam, D., … Bailey, T. S. (2018). A Prospective Multicenter Evaluation of the Accuracy of a Novel Implanted Continuous Glucose Sensor: PRECISE II. Diabetes Technology & Therapeutics, 20(3), 197-206. doi:10.1089/dia.2017.0142Bock, A., François, G., & Gillet, D. (2015). A therapy parameter-based model for predicting blood glucose concentrations in patients with type 1 diabetes. Computer Methods and Programs in Biomedicine, 118(2), 107-123. doi:10.1016/j.cmpb.2014.12.002Acedo, L., Botella, M., Cortés, J. C., Hidalgo, J. I., Maqueda, E., & Villanueva, R. J. (2018). Swarm hybrid optimization for a piecewise model fitting applied to a glucose model. Journal of Systems and Information Technology, 20(4), 404-416. doi:10.1108/jsit-10-2017-0103Alegre-Sanahuja, J., Cortés, J.-C., Villanueva, R.-J., & Santonja, F.-J. (2017). Predicting mobile apps spread: An epidemiological random network modeling approach. SIMULATION, 94(2), 123-130. doi:10.1177/003754971771260