In the world and in Peru, Acute Respiratory Infections are the main cause of death, especially in the most vulnerable population, children under 5 years of age and older adults. Pneumonia is the leading cause of death of children in the world. 60.2% of pneumonia cases affect children under 5 years of age. Thus, prevention and timely treatment of lung diseases are crucial to reduce infant mortality in Peru. Among the main problems associated with this high is percentage the lack of medical professionals and resources, especially in remote areas, such as Puno, Huancavelica and Arequipa, which experience temperatures as low as -20°C during the cold season. This study develops an algorithm based on computational neural networks to differentiate between normal and abnormal lung sounds. The initial base of 917 sounds was used, through a process of data augmentation, this base was increased to 8253 sounds in total, and this process was carried out due to the need of a large number of data for the use of computational neural networks. From each signal, features were extracted using three methods: MFCC, Melspectogram and STFT. Three models were generated, the first one to classify normal and abnormal, which obtained a training Accuracy of 1 and a testing accuracy of 0.998. The second one classifies normal sound, pneumonia and other abnormalities and obtained training Accuracy values of 0.9959 and a testing accuracy of 0.9885. Finally, we classified by specific ailment where we obtained a training Accuracy of 0.9967 and a testing accuracy of 0.9909. This research provides interesting findings about the diagnosis and classification of lung sounds automatically using convolutional neural networks, which is the beginning for the development of a platform to assess the risk of pneumonia in the first moment, thus allowing rapid care and referral that seeks to reduce mortality associated mainly with pneumonia.
This document presents a preliminary study about a pilot deployment of a web service. The service is used as means to raise awareness in university campuses prior to blood donation campaigns and to measure its effect into posterior donor enrollment. The measure the level of awareness a score range from zero to four inclusive was set. It was quantified before and after giving the information. This allowed evaluating the score change influenced by the received information. Another important metric was the contrast between the community participation between the blood donation campaigns at 12 th June 2018 and June 2017. During these campaigns 41 and 25 blood units were collected following the new approach and the traditional way respectively. This variation represents an increase of 64% with respect to the campaign carried out in 2017 by INSN-SB, where the only variation was the use of the application YAWARweb. Moreover, in 2018 there were 36 people interested to donate. Nonetheless, it was not possible because of insufficient hemoglobin, narrow veins, and other causes. This research has as goal to evaluate the usage of our survey through a web service as a tool to raise awareness in university campuses prior to blood donation campaigns. This survey will provide information to the participants about the benefits of blood donation. Thus, creating an incentive to participate in the campaigns and getting the results as an increment of the number of participants. Our group keeps working on preventive health and changing the picture of blood donation leveraged by technology development. The document starts with a general summary of the situation of blood donation in Peru, and then it analyzes the population where the tool is applied. It then proceeds to the methodology of implementation of YAWARweb. Finally, it presents the results of the use of the web application in the community as a method of raising awareness.
This exploratory observational study analyzes the variation of the total amount of vertical electrons (vTEC) in the ionosphere, 17 days before telluric events with grades greater than M7.0 between 2015 and 2016. Thirty telluric events have been analyzed with these characteristics. The data was obtained from 55 satellites and 300 GPS receivers that were downloaded from the Center for Orbit Determination in Europe (CODE). The variations are considered significant only if it is outside the "normal" ranges considered after the statistical analysis performed. The data was downloaded by a program developed in our laboratory. The downloaded data was processed and maps of variations of vTEC generated with a periodicity of 2 hours. The analysis area was considered to be a circular one with a radius of 1000km centered on the epicenter of each earthquake. Variation of vTEC was found during 2015-2016 in 100% of the earthquakes in the range from day 1 to day 17 days before the event, over the circular area of 1000 km radius centered on the epicenter of the earthquake. Of these in 96.55% there are positive variations and a negative one exist in 68.97% of the events. If we observe in the range from day 3 to 17 before the event, a variation was recorded in 100% of the cases, and from day 8 to day 17 before the event in 93.10% of the cases, it is important to emphasize that while the evidence in a period before the event is more likely to find evidence to develop early warning tool for earthquake prevention. This study explores the variation of vTEC as precursor events to each earthquake during 2015-2016; it is a preliminary analysis that shows us the feasibility of analyzing this information as a preamble for an exhaustive association study later. The final objective is to calculate the risk of telluric events which would benefit the population worldwide.
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