Structured and unstructured data integration with electronic medical records

Medicine is a field with high volatility of changes. Everyday new discoveries and procedures are tested with the sole goal of providing a better-quality life to patients. With the evolution of computer science, multiple fields saw an increase of productivity and solutions that could be implemented. More specifically, in medicine new techniques started being tested in order to understand how the systems and practices used can reach higher performances, while maintaining the predefined high standards of quality. For many years data generated in hospital was collected and stored yet few tools were implemented to extract knowledge or any type of advantage. One of the areas that successfully implemented in medicine was the usage of data processing tools and techniques to further extract information regarding the high abundance of data generated in a daily basis, in this field of work. This data can be stored in different ways which leads to multiple approaches on how to deals with it. The sole purpose of this paper is to give an overview of some case studies where structured and unstructured data was used, joint and separately and the value of it.info:eu-repo/semantics/publishedVersio

Collective Almost Synchronization in Complex Networks

This work introduces the phenomenon of Collective Almost Synchronization (CAS), which describes a universal way of how patterns can appear in complex networks even for small coupling strengths. The CAS phenomenon appears due to the existence of an approximately constant local mean field and is characterized by having nodes with trajectories evolving around periodic stable orbits. Common notion based on statistical knowledge would lead one to interpret the appearance of a local constant mean field as a consequence of the fact that the behavior of each node is not correlated to the behaviors of the others. Contrary to this common notion, we show that various well known weaker forms of synchronization (almost, time-lag, phase synchronization, and generalized synchronization) appear as a result of the onset of an almost constant local mean field. If the memory is formed in a brain by minimising the coupling strength among neurons and maximising the number of possible patterns, then the CAS phenomenon is a plausible explanation for it.Comment: 3 figure

Mathematical model of brain tumour with glia-neuron interactions and chemotherapy treatment

Acknowledgements This study was possible by partial financial support from the following Brazilian government agencies: Fundação Araucária, EPSRC-EP/I032606/1 and CNPq, CAPES and Science Without Borders Program Process nos. 17656125, 99999.010583/2013-00 and 245377/2012-3.Peer reviewedPreprin

Reconstruction of eye movements during blinks

In eye movement research in reading, the amount of data plays a crucial role for the validation of results. A methodological problem for the analysis of the eye movement in reading are blinks, when readers close their eyes. Blinking rate increases with increasing reading time, resulting in high data losses, especially for older adults or reading impaired subjects. We present a method, based on the symbolic sequence dynamics of the eye movements, that reconstructs the horizontal position of the eyes while the reader blinks. The method makes use of an observed fact that the movements of the eyes before closing or after opening contain information about the eyes movements during blinks. Test results indicate that our reconstruction method is superior to methods that use simpler interpolation approaches. In addition, analyses of the reconstructed data show no significant deviation from the usual behavior observed in readers

Artificial Neural Network for Predicting Silicon Content in the Hot Metal Produced in a Blast Furnace Fueled by Metallurgical Coke

The main production route for cast iron and steel is through the blast furnace. The silicon content in cast iron is an important indicator of the thermal condition of a blast furnace. High silicon contents indicate an increase in the furnace\u2019s thermal input and, in some cases, may indicate an excess of coke in the reactor. As coke costs predominate in the production of cast iron, tighter control of the silicon content therefore has economic advantages. The main objective of this article was to design an artificial neural network to predict the silicon content in hot metal, varying the number of neurons in the hidden layer by 10, 20, 25, 30, 40, 50, 75, 100, 125, 150, 170 and 200 neurons. In general, all neural networks showed excellent results, with the network with 30 neurons showing the best results among the 12 modeled networks. The validation of the models was confirmed using the Mean Square Error (MSE) and Pearson\u2019s correlation coefficient. The cross-validation technique was used to re-evaluate the performance of neural networks. In short, neural networks can be used in practical operations due to the excellent correlations between the real values and those calculated by the neural network

Complementary action of chemical and electrical synapses to perception

Acknowledgements This study was possible by partial financial support from the following agencies: Fundação Araucária, EPSRC-EP/I032606/1, CNPq No. 441553/2014-1, CAPES No. 17656-12-5 and Science Without Borders Program— Process Nos. 17656125, 99999.010583/2013-00 and 245377/2012-3.Peer reviewedPostprin