A Case–Control Study of Socio-Economic and Nutritional Characteristics as Determinants of Dental Caries in Different Age Groups, Considered as Public Health Problem: Data from NHANES 2013–2014

One of the principal conditions that affects oral health worldwide is dental caries, occurring in about 90% of the global population. This pathology has been considered a challenge because of its high prevalence, besides being a chronic but preventable disease which can be caused by a series of different demographic, dietary, among others. Based on this problem, in this research a demographic and dietary features analysis is performed for the classification of subjects according to their oral health status based on caries, according to the age group where the population belongs, using as feature selector a technique based on fast backward selection (FBS) approach for the development of three predictive models, one for each age range (group 1: 10–19; group 2: 20–59; group 3: 60 or more years old). As validation, a net reclassification improvement (NRI), AUC, ROC, and OR values are used to evaluate their classification accuracy. We analyzed 189 demographic and dietary features from National Health and Nutrition Examination Survey (NHANES) 2013–2014. Each model obtained statistically significant results for most features and narrow OR confidence intervals. Age group 2 obtained a mean NRI = −0.080 and AUC = 0.933; age group 3 obtained a mean NRI = −0.024 and AUC = 0.787; and age group 4 obtained a mean NRI = −0.129 and AUC = 0.735. Based on these results, it is concluded that these specific demographic and dietary features are significant determinants for estimating the oral health status in patients based on their likelihood of developing caries, and the age group could imply different risk factors for subject

A Case–Control Study of Socio-Economic and Nutritional Characteristics as Determinants of Dental Caries in Different Age Groups, Considered as Public Health Problem: Data from NHANES 2013–2014

One of the principal conditions that affects oral health worldwide is dental caries, occurring in about 90% of the global population. This pathology has been considered a challenge because of its high prevalence, besides being a chronic but preventable disease which can be caused by a series of different demographic, dietary, among others. Based on this problem, in this research a demographic and dietary features analysis is performed for the classification of subjects according to their oral health status based on caries, according to the age group where the population belongs, using as feature selector a technique based on fast backward selection (FBS) approach for the development of three predictive models, one for each age range (group 1: 10–19; group 2: 20–59; group 3: 60 or more years old). As validation, a net reclassification improvement (NRI), AUC, ROC, and OR values are used to evaluate their classification accuracy. We analyzed 189 demographic and dietary features from National Health and Nutrition Examination Survey (NHANES) 2013–2014. Each model obtained statistically significant results for most features and narrow OR confidence intervals. Age group 2 obtained a mean NRI = −0.080 and AUC = 0.933; age group 3 obtained a mean NRI = −0.024 and AUC = 0.787; and age group 4 obtained a mean NRI = −0.129 and AUC = 0.735. Based on these results, it is concluded that these specific demographic and dietary features are significant determinants for estimating the oral health status in patients based on their likelihood of developing caries, and the age group could imply different risk factors for subject

BookSense an Application for Mental Disorders Diagnosis: A Case Study for User Evaluation and Redesign

Booksense, a mobile application that allows to identify mental disorders such as depression, work stress and postraumatism [13], through a series of questions based on a mental health assessment that allows you to nd out if you have a mental illness, the app can detect if the user shows signs of a mental disorder, being the most important to detect the problem from its stages initials, plus it also has a database of institutions in the country where you can receive care. The World Health Organization (WHO) estimates that there are currently 300 million people on the planet who su er from depression. This is why it is important to have assisted diagnostic tools that help prevent this type of a ectations in the population, as well as keep informed. the people about help centers. All this would not be possible if you do not count an application that has three important aspects that are: E ciency, e ectiveness and satisfaction aspects that are not present in this diagnostic tool is why the importance of the use of usability evaluations. This research aims to generate a redesign of this application based on certain heuristics that ll the gaps in usabilityBooksense, una aplicación móvil que permite identificar trastornos mentales como depresión, estrés laboral y postraumatismo [13], a través de una serie de preguntas basadas en una evaluación de salud mental que te permite saber si tienes una enfermedad mental, la aplicación puede detectar si el usuario muestra signos de un trastorno mental, siendo lo más importante para detectar el problema desde sus etapas iniciales, además también cuenta con una base de datos de instituciones en el país donde puede recibir atención. La Organización Mundial de la Salud (OMS) estima que actualmente hay 300 millones de personas en el planeta que padecen depresión. Por eso es importante contar con herramientas de diagnóstico asistido que ayuden a prevenir este tipo de afectaciones en la población, así como a mantenerse informada. la gente sobre los centros de ayuda. Todo esto no sería posible si no se cuenta una aplicación que tiene tres aspectos importantes que son: Aspectos de eficiencia, efectividad y satisfacción que no están presentes en esta herramienta de diagnóstico de ahí la importancia del uso de evaluaciones de usabilidad. Esta investigación tiene como objetivo generar un rediseño de esta aplicación en base a ciertas heurísticas que llenen los vacíos de usabilida

Front-End Design Guidelines for Infotainment Systems

This paper presents a set of front-end design guidelines intended to provide a starting point to designers of user interfaces for infotainment systems. The proposed approach suggests guidance on four dimensions inferred from state of the art such as crucial to achieve well designed automotive interfaces: a) Design; b) Interaction; c) Security; and d) Connectivity. Guidelines were thought by integrating conceptual-insights from Graphic Design; User Centered Design; Human-Machine Interfaces; Usability; and Human-Computer interaction. Additionally, were specified and structured to be used also as a comparing tool (Like Heuristic- Evaluation technique) to analyze front-end of existent infotainment systems. Said duality allowed to revise the pertinence of the proposal through a case study where 30 participants (25 regular users and 5 technicalexperts) compared suggested guidelines’ specification against interactions provided by the front–end of Mazda Connect© infotainment System. Obtained results suggested that setting of proposed guidelines was compatible with participants’ perceptions facilitating to identify pain-points on current design; thus, proposed guidance could scaffold base-insights for new front-end designs

BookSense an Application for Mental Disorders Diagnosis: A Case Study for User Evaluation and Redesign

Booksense, a mobile application that allows to identify mental disorders such as depression, work stress and postraumatism [13], through a series of questions based on a mental health assessment that allows you to nd out if you have a mental illness, the app can detect if the user shows signs of a mental disorder, being the most important to detect the problem from its stages initials, plus it also has a database of institutions in the country where you can receive care. The World Health Organization (WHO) estimates that there are currently 300 million people on the planet who su er from depression. This is why it is important to have assisted diagnostic tools that help prevent this type of a ectations in the population, as well as keep informed. the people about help centers. All this would not be possible if you do not count an application that has three important aspects that are: E ciency, e ectiveness and satisfaction aspects that are not present in this diagnostic tool is why the importance of the use of usability evaluations. This research aims to generate a redesign of this application based on certain heuristics that ll the gaps in usabilityBooksense, una aplicación móvil que permite identificar trastornos mentales como depresión, estrés laboral y postraumatismo [13], a través de una serie de preguntas basadas en una evaluación de salud mental que te permite saber si tienes una enfermedad mental, la aplicación puede detectar si el usuario muestra signos de un trastorno mental, siendo lo más importante para detectar el problema desde sus etapas iniciales, además también cuenta con una base de datos de instituciones en el país donde puede recibir atención. La Organización Mundial de la Salud (OMS) estima que actualmente hay 300 millones de personas en el planeta que padecen depresión. Por eso es importante contar con herramientas de diagnóstico asistido que ayuden a prevenir este tipo de afectaciones en la población, así como a mantenerse informada. la gente sobre los centros de ayuda. Todo esto no sería posible si no se cuenta una aplicación que tiene tres aspectos importantes que son: Aspectos de eficiencia, efectividad y satisfacción que no están presentes en esta herramienta de diagnóstico de ahí la importancia del uso de evaluaciones de usabilidad. Esta investigación tiene como objetivo generar un rediseño de esta aplicación en base a ciertas heurísticas que llenen los vacíos de usabilida

Métricas de Registro de Imágenes y Predicción de Dolor de Rodilla por Osteoartritis Crónica: Datos de la Osteoarthritis Initiative

Osteoarthritis (OA) is the most common type of arthritis, is a growing disease in the industrialized world. OA is an incapacitate disease that affects more than 1 in 10 adults over 60 years old. X-ray medical imaging is a primary diagnose technique used on staging OA that the expert reads and quantify the stage of the disease. Some Computer-Aided Diagnosis (CADx) efforts to automate the OA detection have been made to aid the radiologist in the detection and control, nevertheless, the pain inherits to the disease progression is left behind. In this research, it’s proposed a CADx system that quantify the bilateral similarity of the patient’s knees to correlate the degree of asymmetry with the pain development. Firstly, the knee images were aligned using a B-spline image registration algorithm, then, a set of similarity measures were quantified, lastly, using this measures it’s proposed a multivariate model to predict the pain development up to 48 months. The methodology was validated on a cohort of 131 patients from the Osteoarthritis Initiative (OAI) database. Results suggest that mutual information can be associated with K&L OAI scores, and Multivariate models predicted knee chronic pain with: AUC 0.756, 0.704, 0.713 at baseline, one year, and two years’ follow-up.Osteoarthritis (OA) is the most common type of arthritis, is a growing disease in the industrialized world. OA is an incapacitate disease that affects more than 1 in 10 adults over 60 years old. X-ray medical imaging is a primary diagnose technique used on staging OA that the expert reads and quantify the stage of the disease. Some Computer-Aided Diagnosis (CADx) efforts to automate the OA detection have been made to aid the radiologist in the detection and control, nevertheless, the pain inherits to the disease progression is left behind. In this research, it’s proposed a CADx system that quantify the bilateral similarity of the patient’s knees to correlate the degree of asymmetry with the pain development. Firstly, the knee images were aligned using a B-spline image registration algorithm, then, a set of similarity measures were quantified, lastly, using this measures it’s proposed a multivariate model to predict the pain development up to 48 months. The methodology was validated on a cohort of 131 patients from the Osteoarthritis Initiative (OAI) database. Results suggest that mutual information can be associated with K&L OAI scores, and Multivariate models predicted knee chronic pain with: AUC 0.756, 0.704, 0.713 at baseline, one year, and two years’ follow-up

A comparison of back propagation and Generalized Regression Neural Networks performance in neutron spectrometry

The process of unfolding the neutron energy spectrum has been subject of research for many years. Monte Carlo, iterative methods, the bayesian theory, the principle of maximum entropy are some of the methods used. The drawbacks associated with traditional unfolding procedures have motivated the research of complementary approaches. Back Propagation Neural Networks (BPNN), have been applied with success in neutron spectrometry and dosimetry domains, however, the structure and learning parameters are factors that highly impact in the networks performance. In ANN domain, Generalized Regression Neural Network (GRNN) is one of the simplest neural networks in term of network architecture and learning algorithm. The learning is instantaneous, requiring no time for training. Opposite to BPNN, a GRNN would be formed instantly with just a 1-pass training on the development data. In the network development phase, the only hurdle is to optimize the hyper-parameter, which is known as sigma, governing the smoothness of the network. The aim of this work was to compare the performance of BPNN and GRNN in the solution of the neutron spectrometry problem. From results obtained it can be observed that despite the very similar results, GRNN performs better than BPNN

Multivariate feature selection of image descriptors data for breast cancer with computer-assisted diagnosis

Breast cancer is an important global health problem, and the most common type of cancer among women. Late diagnosis significantly decreases the survival rate of the patient; however, using mammography for early detection has been demonstrated to be a very important tool increasing the survival rate. The purpose of this paper is to obtain a multivariate model to classify benign and malignant tumor lesions using a computer-assisted diagnosis with a genetic algorithm in training and test datasets from mammography image features. A multivariate search was conducted to obtain predictive models with different approaches, in order to compare and validate results. The multivariate models were constructed using: Random Forest, Nearest centroid, and K-Nearest Neighbor (K-NN) strategies as cost function in a genetic algorithm applied to the features in the BCDR public databases. Results suggest that the two texture descriptor features obtained in the multivariate model have a similar or better prediction capability to classify the data outcome compared with the multivariate model composed of all the features, according to their fitness value. This model can help to reduce the workload of radiologists and present a second opinion in the classification of tumor lesions

Speed Bump Detection Using Accelerometric Features: A Genetic Algorithm Approach

AmongthecurrentchallengesoftheSmartCity,trafficmanagementandmaintenanceareof utmostimportance. Roadsurfacemonitoringiscurrentlyperformedbyhumans,buttheroadsurface condition is one of the main indicators of road quality, and it may drastically affect fuel consumption and the safety of both drivers and pedestrians. Abnormalities in the road, such as manholes and potholes, can cause accidents when not identified by the drivers. Furthermore, human-induced abnormalities, such as speed bumps, could also cause accidents. In addition, while said obstacles ought to be signalized according to specific road regulation, they are not always correctly labeled. Therefore, we developed a novel method for the detection of road abnormalities (i.e., speed bumps). This method makes use of a gyro, an accelerometer, and a GPS sensor mounted in a car. After having the vehicle cruise through several streets, data is retrieved from the sensors. Then, using a cross-validation strategy, a genetic algorithm is used to find a logistic model that accurately detects road abnormalities. The proposed model had an accuracy of 0.9714 in a blind evaluation, with a false positive rate smaller than 0.018, and an area under the receiver operating characteristic curve of 0.9784. This methodology has the potential to detect speed bumps in quasi real-time conditions, and can be used to construct a real-time surface monitoring system.AmongthecurrentchallengesoftheSmartCity,trafficmanagementandmaintenanceareof utmostimportance. Roadsurfacemonitoringiscurrentlyperformedbyhumans,buttheroadsurface condition is one of the main indicators of road quality, and it may drastically affect fuel consumption and the safety of both drivers and pedestrians. Abnormalities in the road, such as manholes and potholes, can cause accidents when not identified by the drivers. Furthermore, human-induced abnormalities, such as speed bumps, could also cause accidents. In addition, while said obstacles ought to be signalized according to specific road regulation, they are not always correctly labeled. Therefore, we developed a novel method for the detection of road abnormalities (i.e., speed bumps). This method makes use of a gyro, an accelerometer, and a GPS sensor mounted in a car. After having the vehicle cruise through several streets, data is retrieved from the sensors. Then, using a cross-validation strategy, a genetic algorithm is used to find a logistic model that accurately detects road abnormalities. The proposed model had an accuracy of 0.9714 in a blind evaluation, with a false positive rate smaller than 0.018, and an area under the receiver operating characteristic curve of 0.9784. This methodology has the potential to detect speed bumps in quasi real-time conditions, and can be used to construct a real-time surface monitoring system

Multivariate feature selection of image descriptors data for breast cancer with computer-assisted diagnosis

Breast cancer is an important global health problem, and the most common type of cancer among women. Late diagnosis significantly decreases the survival rate of the patient; however, using mammography for early detection has been demonstrated to be a very important tool increasing the survival rate. The purpose of this paper is to obtain a multivariate model to classify benign and malignant tumor lesions using a computer-assisted diagnosis with a genetic algorithm in training and test datasets from mammography image features. A multivariate search was conducted to obtain predictive models with different approaches, in order to compare and validate results. The multivariate models were constructed using: Random Forest, Nearest centroid, and K-Nearest Neighbor (K-NN) strategies as cost function in a genetic algorithm applied to the features in the BCDR public databases. Results suggest that the two texture descriptor features obtained in the multivariate model have a similar or better prediction capability to classify the data outcome compared with the multivariate model composed of all the features, according to their fitness value. This model can help to reduce the workload of radiologists and present a second opinion in the classification of tumor lesions