Ensemble Models of Cutting-Edge Deep Neural Networks for Blood Glucose Prediction in Patients with Diabetes.

This article proposes two ensemble neural network-based models for blood glucose prediction at three different prediction horizons-30, 60, and 120 min-and compares their performance with ten recently proposed neural networks. The twelve models' performances are evaluated under the same OhioT1DM Dataset, preprocessing workflow, and tools at the three prediction horizons using the most common metrics in blood glucose prediction, and we rank the best-performing ones using three methods devised for the statistical comparison of the performance of multiple algorithms: scmamp, model confidence set, and superior predictive ability. Our analysis provides a comparison of the state-of-the-art neural networks for blood glucose prediction, estimating the model's error, highlighting those with the highest probability of being the best predictors, and providing a guide for their use in clinical practice.

Data Based Prediction of Blood Glucose Concentrations Using Evolutionary Methods.

Predicting glucose values on the basis of insulin and food intakes is a difficult task that people with diabetes need to do daily. This is necessary as it is important to maintain glucose levels at appropriate values to avoid not only short-term, but also long-term complications of the illness. Artificial intelligence in general and machine learning techniques in particular have already lead to promising results in modeling and predicting glucose concentrations. In this work, several machine learning techniques are used for the modeling and prediction of glucose concentrations using as inputs the values measured by a continuous monitoring glucose system as well as also previous and estimated future carbohydrate intakes and insulin injections. In particular, we use the following four techniques: genetic programming, random forests, k-nearest neighbors, and grammatical evolution. We propose two new enhanced modeling algorithms for glucose prediction, namely (i) a variant of grammatical evolution which uses an optimized grammar, and (ii) a variant of tree-based genetic programming which uses a three-compartment model for carbohydrate and insulin dynamics. The predictors were trained and tested using data of ten patients from a public hospital in Spain. We analyze our experimental results using the Clarke error grid metric and see that 90% of the forecasts are correct (i.e., Clarke error categories A and B), but still even the best methods produce 5 to 10% of serious errors (category D) and approximately 0.5% of very serious errors (category E). We also propose an enhanced genetic programming algorithm that incorporates a three-compartment model into symbolic regression models to create smoothed time series of the original carbohydrate and insulin time series.

Available medical choices for the management of menopause.

The age at which menopause occurs is a critical factor in the magnitude of its consequences. Most of the medium-to-long-term effects of oestrogen deprivation depend on their duration. The timing of the last menstruation is therefore important, but hypoestrogenic amenorrhoea during the reproductive age is also a relevant factor in the evaluation of individual risks. In recent years, moving post-menopausal women from the lowest point of ovarian hypofunction has been the most important motivation for developing guidelines for the hormonal management of menopause. However, recent data suggest that this may be associated with an unacceptable increase in morbidity in a number of women. Concerns about long-term hormone replacement therapy (HRT) at menopause have recently enhanced interest in a group of molecules that act on the oestrogen receptor with selective effects, known as selective oestrogen receptor modulators (SERMs). Of these, Raloxifene has been approved for the treatment and prevention of osteoporosis, and exhibits a pattern of actions particularly well matched to the needs and concerns of post-menopausal women. Further studies on SERMs may open up new vistas in patient-specific management of post-menopausal health. Finally, debates on the specific health consequences of menopause deal mainly with the risk of chronic disease. Gynaecologists and other health professionals would be advised to develop intervention strategies at menopause according to the continuum of a woman's life, beginning at the post-menarche and extending into later life.