A machine-learning approach to predict postprandial hypoglycemia

Background For an effective artificial pancreas (AP) system and an improved therapeutic intervention with continuous glucose monitoring (CGM), predicting the occurrence of hypoglycemia accurately is very important. While there have been many studies reporting successful algorithms for predicting nocturnal hypoglycemia, predicting postprandial hypoglycemia still remains a challenge due to extreme glucose fluctuations that occur around mealtimes. The goal of this study is to evaluate the feasibility of easy-to-use, computationally efficient machine-learning algorithm to predict postprandial hypoglycemia with a unique feature set. Methods We use retrospective CGM datasets of 104 people who had experienced at least one hypoglycemia alert value during a three-day CGM session. The algorithms were developed based on four machine learning models with a unique data-driven feature set: a random forest (RF), a support vector machine using a linear function or a radial basis function, a K-nearest neighbor, and a logistic regression. With 5-fold cross-subject validation, the average performance of each model was calculated to compare and contrast their individual performance. The area under a receiver operating characteristic curve (AUC) and the F1 score were used as the main criterion for evaluating the performance. Results In predicting a hypoglycemia alert value with a 30-min prediction horizon, the RF model showed the best performance with the average AUC of 0.966, the average sensitivity of 89.6%, the average specificity of 91.3%, and the average F1 score of 0.543. In addition, the RF showed the better predictive performance for postprandial hypoglycemic events than other models. Conclusion In conclusion, we showed that machine-learning algorithms have potential in predicting postprandial hypoglycemia, and the RF model could be a better candidate for the further development of postprandial hypoglycemia prediction algorithm to advance the CGM technology and the AP technology further.11Ysciescopu

Machine Learning-Based Analysis of Adolescent Gambling Factors

Background and aims: Problem gambling among adolescents has recently attracted attention because of easy access to gambling in online environments and its serious effects on adolescent lives. We proposed a machine learning-based analysis method for predicting the degree of problem gambling. Methods: Of the 17,520 respondents in the 2018 National Survey on Youth Gambling Problems dataset (collected by the Korea Center on Gambling Problems), 5,045 students who had gambled in the past 3 months were included in this study. The Gambling Problem Severity Scale was used to provide the binary label information. After the random forest-based feature selection method, we trained four models: random forest (RF), support vector machine (SVM), extra trees (ETs), and ridge regression. Results: The online gambling behavior in the past 3 months, experience of winning money or goods, and gambling of personal relationship were three factors exhibiting the high feature importance. All four models demonstrated an area under the curve (AUC) of >0.7; ET showed the highest AUC (0.755), RF demonstrated the highest accuracy (71.8%), and SVM showed the highest F1 score (0.507) on a testing set. Discussion: The results indicate that machine learning models can convey meaningful information to support predictions regarding the degree of problem gambling. Conclusion: Machine learning models trained using important features showed moderate accuracy in a large-scale Korean adolescent dataset. These findings suggest that the method will help screen adolescents at risk of problem gambling. We believe that expandable machine learning-based approaches will become more powerful as more datasets are collected.11Ysciessciscopu

Deep ECG-Respiration Network (DeepER Net) for Recognizing Mental Stress

Unmanaged long-term mental stress in the workplace can lead to serious health problems and reduced productivity. To prevent this, it is important to recognize and relieve mental stress in a timely manner. Here, we propose a novel stress detection algorithm based on end-to-end deep learning using multiple physiological signals, such as electrocardiogram (ECG) and respiration (RESP) signal. To mimic workplace stress in our experiments, we used Stroop and math tasks as stressors, with each stressor being followed by a relaxation task. Herein, we recruited 18 subjects and measured both ECG and RESP signals using Zephyr BioHarness 3.0. After five-fold cross validation, the proposed network performed well, with an average accuracy of 83.9%, an average F1 score of 0.81, and an average area under the receiver operating characteristic (ROC) curve (AUC) of 0.92, demonstrating its superiority over conventional machine learning models. Furthermore, by visualizing the activation of the trained network’s neurons, we found that they were activated by specific ECG and RESP patterns. In conclusion, we successfully validated the feasibility of end-to-end deep learning using multiple physiological signals for recognition of mental stress in the workplace. We believe that this is a promising approach that will help to improve the quality of life of people suffering from long-term work-related mental stress

Deep ECG-Respiration Network (DeepER Net) for Recognizing Mental Stress

Unmanaged long-term mental stress in the workplace can lead to serious health problems and reduced productivity. To prevent this, it is important to recognize and relieve mental stress in a timely manner. Here, we propose a novel stress detection algorithm based on end-to-end deep learning using multiple physiological signals, such as electrocardiogram (ECG) and respiration (RESP) signal. To mimic workplace stress in our experiments, we used Stroop and math tasks as stressors, with each stressor being followed by a relaxation task. Herein, we recruited 18 subjects and measured both ECG and RESP signals using Zephyr BioHarness 3.0. After five-fold cross validation, the proposed network performed well, with an average accuracy of 83.9%, an average F1 score of 0.81, and an average area under the receiver operating characteristic (ROC) curve (AUC) of 0.92, demonstrating its superiority over conventional machine learning models. Furthermore, by visualizing the activation of the trained network's neurons, we found that they were activated by specific ECG and RESP patterns. In conclusion, we successfully validated the feasibility of end-to-end deep learning using multiple physiological signals for recognition of mental stress in the workplace. We believe that this is a promising approach that will help to improve the quality of life of people suffering from long-term work-related mental stress.11Ysciescopu

tDCS Electrode Positioning Strategy with Simulation

1

Deep neural networks for predicting blood glucose level of virtual patients with type 1 diabetes

1

Facial Expression Recognition With Deep Learning Approach For Detecting Stress

A workplace stress detection system using face images require a low-cost installation and does not interfere with the movements of workers, thus the system will be the better promising approach than physiological data based approach using wearable devices. As a preliminary research, we proposed a methodology for emotion recognition to detect workplace stress. We trained various structures of convolutional neural networks (CNNs) with real-world affective image dataset (RAF-DB). Among the trained CNNs, VGG16 showed the highest accuracy on test set. In order to further improve the classification performance, we used generative adversarial network (GAN) as a data augmentation method. After post-processing generated images by the GAN, we fine-tuned VGG16 with mixed dataset, which included original training images and post-processed generated images, and then we found that the classification performance was slightly improved. In conclusion, The developed network showed 78.5 % accuracy for face emotion and is expected to be used in algorithms for stress detection in the future.1

Towards Interpretation for Blood Glucose Level Prediction Models

Patients with diabetes need to manage their blood glucose (BG) level to prevent diabetic complications such as retinopathy and cardiovascular diseases. We developed tree-based machine learning (ML) and deep learning (DL) models with continuous glucose monitoring (CGM) data points to improve the BG management. We extracted 20 CGM time series from 20 virtual patients with type 1 diabetes generat-ed by UVA/Padova Type 1 Diabetes Metabolic Simulator, set 12 CGM data points as input, and the CGM data point after 30-min prediction horizon as output. The long short-term memory showed the lowest average root mean squared error (17.37 mg/dL) and mean absolute percentage error (8.33 %). In the clinical analysis, the deep neural network showed the highest percentage in region A (92.53 %) of Clarke error grid analysis (CEGA) and all models had the high percentage in region A and B (> 99 %) of CEGA. Then, we analyzed each model’s feature importance and found that the models exhib-ited different feature importance. We believe that the presented method will help to manage BG levels of patients with diabetes and to interpret the BG level predictive models.1

An ensemble approach for accurately predicting hypoglycemia

1

Personalized Glucose Prediction Algorithm (PGPA) With A Support Vector Regression

Personalized glucose prediction algorithm (PGPA) is considered as an excellent approach to manage glucose levels due to abilities to consider a patient‘s non-linear glucose patterns. To extract continuous glucose monitoring (CGM) time-series data, 30 virtual patients with type 1 diabetes were generated by UVA/Padova T1DMS. The developed support vector regression that was trained with CGM points collected for 3 days showed 17.7 mg/dL of root mean square errors and 11.6 % of mean absolute percentage error on average. In conclusion, we validated the approach of PGPA with the patients and it will be greatly helpful to manage blood glucose level.2