A Deep Neural Network Application for Improved Prediction of $\text{HbA}_{\text{1c}}$ in Type 1 Diabetes
| Autor: | Aleksandr Zaitcev, Mohammed Benaissa, Tim Good, Jackie Elliott, Mohammad R. Eissa, Zheng Hui |
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| Rok vydání: | 2020 |
| Předmět: |
020205 medical informatics
Artificial neural network Mean absolute error 030209 endocrinology & metabolism Missing periods 02 engineering and technology Computer Science Applications Combinatorics 03 medical and health sciences 0302 clinical medicine Diabetes control Health Information Management 0202 electrical engineering electronic engineering information engineering Electrical and Electronic Engineering Biotechnology Mathematics |
| Zdroj: | IEEE Journal of Biomedical and Health Informatics. 24:2932-2941 |
| ISSN: | 2168-2208 2168-2194 |
| DOI: | 10.1109/jbhi.2020.2967546 |
| Popis: | $\text{HbA}_{\text{1c}}$ is a primary marker of long-term average blood glucose, which is an essential measure of successful control in type 1 diabetes. Previous studies have shown that $\text{HbA}_{\text{1c}}$ estimates can be obtained from 5-12 weeks of daily blood glucose measurements. However, these methods suffer from accuracy limitations when applied to incomplete data with missing periods of measurements. The aim of this article is to overcome these limitations improving the accuracy and robustness of $\text{HbA}_{\text{1c}}$ prediction from time series of blood glucose. A novel data-driven $\text{HbA}_{\text{1c}}$ prediction model based on deep learning and convolutional neural networks is presented. The model focuses on the extraction of behavioral patterns from sequences of self-monitored blood glucose readings on various temporal scales. Assuming that subjects who share behavioral patterns have also similar capabilities for diabetes control and resulting $\text{HbA}_{\text{1c}}$ , it becomes possible to infer the $\text{HbA}_{\text{1c}}$ of subjects with incomplete data from multiple observations of similar behaviors. Trained and validated on a dataset, containing 1543 real world observation epochs from 759 subjects, the model has achieved the mean absolute error of 4.80 $\pm\; \text{0.62}$ mmol/mol, median absolute error of 3.81 $\pm\; \text{0.58}$ mmol/mol and $\text{R}^2$ of 0.71 $\pm$ 0.09 on average during the 10 fold cross validation. Automatic behavioral characterization via extraction of sequential features by the proposed convolutional neural network structure has significantly improved the accuracy of $\text{HbA}_{\text{1c}}$ prediction compared to the existing methods. |
| Databáze: | OpenAIRE |
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