Predicting Glucose Levels in Patients with Type1 Diabetes Based on Physiological and Activity Data
| Autor: | Andrew J. Padilla, Mohammad Pourhomayoun, Chris Fong, Sameer Kulkarni, Boyi Jiang, Koenrad B. MacBride, Mohammad Reza Vahedi, Siddharth Arunachalam, Alex Zhong, Yosep Kim, Woo Wunsik |
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| Rok vydání: | 2018 |
| Předmět: |
Type 1 diabetes
medicine.medical_specialty Computer science 010401 analytical chemistry Physical activity 030209 endocrinology & metabolism Feature selection Hypoglycemia medicine.disease 01 natural sciences 0104 chemical sciences 03 medical and health sciences 0302 clinical medicine Internal medicine Diabetes mellitus medicine Cardiology In patient Data pre-processing Glycemic |
| Zdroj: | Proceedings of the 8th ACM MobiHoc 2018 Workshop on Pervasive Wireless Healthcare Workshop. |
| Popis: | Managing blood glucose levels for type 1 diabetes patients is an absolute necessity to better glycemic control. In this paper, we present a predictive model that uses physiological measurements and physical activity to predict continuous glucose levels and help patients reduce and prevent hyperglycemia and hypoglycemia exposure, conditions that are harmful to patient health.The data of this research includes 4 months of physiological measurements, physical activity, and nutrition information collected from 93 patients with diabetes using the Medtronic MiniMed™ 530G insulin delivery system with Enlite™ sensor. After data preprocessing, missing value imputation, feature extraction, and feature selection, a set of 180 features were derived to represent the raw data. Then, an appropriate predictive model was developed based on machine-learning algorithms to predict continuous glucose levels. The prediction accuracy and error have been calculated to evaluate the performance of the system. The results demonstrated that the predicted glucose levels closely followed the actual sensor glucose (SG) values measured by subcutaneous glucose sensor. |
| Databáze: | OpenAIRE |
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