Daytime hypoglycemia should be accurately predicted to achieve normoglycemia and to avoid disastrous situations. Hypoglycemia, an abnormally low blood glucose level, is divided into daytime hypoglycemia and nocturnal hypoglycemia. Many studies of hypoglycemia prevention deal with nocturnal hypoglycemia. In this paper, we propose new predictor variables to predict daytime hypoglycemia using continuous glucose monitoring (CGM) data. We apply classification and regression tree (CART) as a prediction method. The independent variables of our prediction model are the rate of decrease from a peak and absolute level of the BG at the decision point. The evaluation results showed that our model was able to detect almost 80% of hypoglycemic events 15 min in advance, which was higher than the existing methods with similar conditions. The proposed method might achieve a real-time prediction as well as can be embedded into BG monitoring device.
For many people, measuring blood glucose currently involves pricking a finger with a needle attached to a device. The blood sample is then analyzed by a continuous glucose monitor (CGM), which often needs to be calibrated at least twice a day. The process can be difficult, uncomfortable, and inconvenient, especially for children and people who need to test their blood in the middle of the night. As a result, some people are unable to measure their levels as often or as accurately as necessary. The researchers behind the current study hope that a noninvasive method will help improve compliance rates, particularly among those who need to monitor their glucose levels closely, such as people with diabetes.
Discussion on use of artificial intelligence (AI) and health specifically is ubiquitous in the medical and lay press reflecting the perception that it has enormous potential to reduce the personal and global burden of many long-term medical conditions. Currently diabetes appears to be the poster child for the application of AI in health care for a number of reasons.1 Worldwide, the number of adults and children developing diabetes continues to rise in parallel with global access to smartphone technologies. On a daily basis, personal data from people living with diabetes are continuously created and logged. Although the main variable of interest is glucose, with the rise in consumer tracking technologies, glucose data are being supplemented with additional information related to nutrition, physical activity, and sleep. With the increasing availability of additional sensor technologies for physiological monitoring including smart insulin pens, social media, and records of internet searches, the diabetes data pool will continue to grow.2,3
Hypoglycemia is common and potentially dangerous among those treated for diabetes. Electronic health records (EHRs) are important resources for hypoglycemia surveillance. In this study, we report the development and evaluation of deep learning-based natural language processing systems to automatically detect hypoglycemia events from the EHR narratives. Experts in Public Health annotated 500 EHR notes from patients with diabetes. We used this annotated dataset to train and evaluate HYPE, supervised NLP systems for hypoglycemia detection. In our experiment, the convolutional neural network model yielded promising performance $Precision=0.96 \pm 0.03, Recall=0.86 \pm 0.03, F1=0.91 \pm 0.03$ in a 10-fold cross-validation setting. Despite the annotated data is highly imbalanced, our CNN-based HYPE system still achieved a high performance for hypoglycemia detection. HYPE could be used for EHR-based hypoglycemia surveillance and to facilitate clinicians for timely treatment of high-risk patients.
From diagnostic fingernail sensors to the use of its Watson platform to help invent new drugs, IBM has impressively positioned itself at the forefront of medical tech. Today, January 3, it announced the latest project in this field: a new mobile app feature that's designed to work as an early warning sign for diabetics about the perils of "going low" on their blood sugar levels. To the uninitiated, keeping these blood sugar levels in check sounds easy: Simply avoid eating food with too much sugar and you're good to go. However, the reality is that things are more complicated than that. A person living with type 1 diabetes has to make upwards of 180 decisions every single day, all of which can affect their well being.