Prediction-Coherent LSTM-based Recurrent Neural Network for Safer Glucose Predictions in Diabetic People

In the context of time-series forecasting, we propose a LSTM-based recurrent neural network architecture and loss function that enhance the stability of the predictions. In particular, the loss function penalizes the model, not only on the prediction error (mean-squared error), but also on the predicted variation error. We apply this idea to the prediction of future glucose values in diabetes, which is a delicate task as unstable predictions can leave the patient in doubt and make him/her take the wrong action, threatening his/her life. The study is conducted on type 1 and type 2 diabetic people, with a focus on predictions made 30-minutes ahead of time. First, we confirm the superiority, in the context of glucose prediction, of the LSTM model by comparing it to other state-of-the-art models (Extreme Learning Machine, Gaussian Process regressor, Support Vector Regressor). Then, we show the importance of making stable predictions by smoothing the predictions made by the models, resulting in an overall improvement of the clinical acceptability of the models at the cost in a slight loss in prediction accuracy. Finally, we show that the proposed approach, outperforms all baseline results. More precisely, it trades a loss of 4.3\% in the prediction accuracy for an improvement of the clinical acceptability of 27.1\%. When compared to the moving average post-processing method, we show that the trade-off is more efficient with our approach

Socioeconomic inequalities in stillbirth rates in Europe: measuring the gap using routine data from the Euro-Peristat Project

Background Previous studies have shown that socioeconomic position is inversely associated with stillbirth risk, but the impact on national rates in Europe is not known. We aimed to assess the magnitude of social inequalities in stillbirth rates in European countries using indicators generated from routine monitoring systems. Methods Aggregated data on the number of stillbirths and live births for the year 2010 were collected for three socioeconomic indicators (mothers’ educational level, mothers’ and fathers’ occupational group) from 29 European countries participating in the Euro-Peristat project. Educational categories were coded using the International Standard Classification of Education (ISCED) and analysed as: primary/lower secondary, upper secondary and postsecondary. Parents’ occupations were grouped using International Standard Classification of Occupations (ISCO-08) major groups and then coded into 4 categories: No occupation or student, Skilled/ unskilled workers, Technicians/clerical/service occupations and Managers/professionals. We calculated risk ratios (RR) for stillbirth by each occupational group as well as the percentage population attributable risks using the most advantaged category as the reference (post-secondary education and professional/managerial occupations). Results Data on stillbirth rates by mothers’ education were available in 19 countries and by mothers’ and fathers’ occupations in 13 countries. In countries with these data, the median RR of stillbirth for women with primary and lower secondary education compared to women with postsecondary education was 1.9 (interquartile range (IQR): 1.5 to 2.4) and 1.4 (IQR: 1.2 to 1.6), respectively. For mothers’ occupations, the median RR comparing outcomes among manual workers with managers and professionals was 1.6 (IQR: 1.0–2.1) whereas for fathers’ occupations, the median RR was 1.4 (IQR: 1.2–1.8). When applied to the entire set of countries with data about mothers’ education, 1606 out of 6337 stillbirths (25 %) would not have occurred if stillbirth rates for all women were the same as for women with post-secondary education in their country. Conclusions Data on stillbirths and socioeconomic status from routine systems showed widespread and consistent socioeconomic inequalities in stillbirth rates in Europe. Further research is needed to better understand differences between countries in the magnitude of the socioeconomic gradient

Shieling Areas: Historical Grazing Pressures and Landscape Responses in Northern Iceland

Historical domestic livestock grazing in sensitivelandscapes has commonly been regarded as a major cause ofland degradation in Iceland. Shieling areas, where milkinglivestock were taken to pasture for the summer, representedone element of grazing management and in this paper weconsider the extent to which historical shieling-based grazingpressure contributed to land degradation. Based on a grazingmodel to assess pressures and tephrochronology -based soilaccumulation rates allied to micromorphology as a proxy forland degradation, our findings suggest that the shieling system contributed to the maintenance of upland vegetationcover and related productivity levels without causing landdegradation from settlement through to ca. AD 1300. As landdegradation accelerated from ca. AD 1477 it is likely thatshieling management continued to operate effectively contributingto the overall resilience of livestock farming