Feature Extraction Via Multiresolution MODWT Analysis in a Rainfall Forecast System

During 30 years, expert meteorologists have been sampling meteorological measurements directly related to the rainfall event, in order to improve the current forecast procedures. This study performs the Feature Extraction and Feature Selection processes to extract the relevant information in the rainfall event. The Feature Extraction has been performed with a Multiresolution Analysis applying the Maxima OverlapWavelet Transform. The selection of the wavelet decomposition, was obtained applying a Sequential Feature Selection algorithm based on General Regression Neural Networks. In this paper, it is also presented a novel architecture to perform short and medium term weather forecasts based on Neural Networks and time series estimation filters. The preliminary results obtained, present this architecture as a feasible alternative to the current forecast procedures performed by super computer simulation centers

Impact of the updating of clinical guidelines for RSV bronchiolitis on the use of diagnostic testing and medications in tertiary hospitals in Colombia

Introduction: the incidence of Respiratory Syncytial Virus (RSV) infection and their variability in the clinical management, make this disease a candidate for monitoring adequate use of resources. The objective of this study was to evaluate the impact of the updating of clinical guidelines for RSV bronchiolitis on the use of diagnostic testing and medications in tertiary hospitals in Colombia. Methods: we performed a cross-sectional study, evaluating the frequencies of drug prescription and medical tests, before (January-December 2016) and after (January to December 2019) of updating and dissemination of a new protocol for the treatment of RSV bronchiolitis in two tertiary hospitals in Colombia. Results: a total of 108 patients with RSV bronchiolitis were included. The demographic characteristics and clinical manifestations were similar in both groups. The length of hospital stays was similar in both groups. We did not find statistically significant differences in the frequency of medical tests. There was a decrease in the use of salbutamol (67.3% pre-protocol vs 51.8% post-protocol; P < .01). There were also significant reductions in the use of nebulized hypertonic saline solution (91.6% vs 82.6% P = 0.004). Conclusion: our results demonstrate that the updating of clinical guidelines for RSV bronchiolitis was effective, as it achieved decreases in the use of bronchodilators and nebulized hypertonic saline solution. It is necessary to continue developing new strategies targeted to increase adherence to guidelines and evaluate the impact on the use of resources

Local Rainfall Forecast System based on Time Series Analysis and Neural Networks

Rainfall is one of the most important events in daily life of human beings. During several decades, scientists have been trying to characterize the weather, current forecasts are based on high complex dynamic models. In this paper is presented a local rainfall forecast system based on Time Series analysis and Neural Networks. This model tries to complement the currently state of the art ensembles, from a locally historical perspective, where the model definition is not so dependent from the exact values of the initial conditions. After several year taking data, expert meteorologists proposed this approximation to characterize the local weather behavior, that is being automated by this system in different stages. However the whole system is introduced, it is focused on the different rainfall events situation classification as well as the time series analysis and forecas