Entity

Time filter

Source Type


Veredas F.J.,University of Malaga | Luque-Baena R.M.,University of Extremadura | Martin-Santos F.J.,Servicio Andaluz de Salud. | Morilla-Herrera J.C.,Servicio Andaluz de Salud. | Morente L.,Escuela Universitaria de Enfermeria
Neurocomputing | Year: 2015

A pressure ulcer is a clinical pathology of localized damage to the skin and underlying tissue caused by pressure, shear or friction. Diagnosis, care and treatment of pressure ulcers can result in extremely expensive costs for health systems. A reliable diagnosis supported by precise wound evaluation is crucial in order to succeed on the treatment decision and, in some cases, to save the patient's life. However, current clinical evaluation procedures, focused mainly on visual inspection, do not seem to be accurate enough to accomplish this important task. This paper presents a computer-vision approach based on image processing algorithms and supervised learning techniques to help detect and classify wound tissue types that play an important role in wound diagnosis. The system proposed involves the use of the k-means clustering algorithm for image segmentation and compares three different machine learning approaches-neural networks, support vector machines and random forest decision trees-to classify effectively each segmented region as the appropriate tissue type. Feature selection based on a wrapper approach with recursive feature elimination is shown to be effective in keeping the efficacy of the classifiers up and significantly reducing the number of necessary predictors. Results obtained show high performance rates from classifiers based on fitted neural networks, random forest models and support vector machines (overall accuracy on a testing set [95% CI], respectively: 81.87% [80.03%, 83.61%]; 87.37% [85.76%, 88.86%]; 88.08% [86.51%, 89.53%]), with significant differences found between the three machine learning approaches. This study seeks to provide, using standard classification algorithms, a consistent and robust methodological framework as a basis for the development of reliable computational systems to support ulcer diagnosis. © 2015 Elsevier B.V. Source


Veredas F.J.,University of Malaga | Mesa H.,Advance Health | Morente L.,Escuela Universitaria de Enfermeria
Medical and Biological Engineering and Computing | Year: 2015

A pressure ulcer is a clinical pathology of localised damage to the skin and underlying tissue caused by pressure, shear or friction. Reliable diagnosis supported by precise wound evaluation is crucial in order to success on treatment decisions. This paper presents a computer-vision approach to wound-area detection based on statistical colour models. Starting with a training set consisting of 113 real wound images, colour histogram models are created for four different tissue types. Back-projections of colour pixels on those histogram models are used, from a Bayesian perspective, to get an estimate of the posterior probability of a pixel to belong to any of those tissue classes. Performance measures obtained from contingency tables based on a gold standard of segmented images supplied by experts have been used for model selection. The resulting fitted model has been validated on a training set consisting of 322 wound images manually segmented and labelled by expert clinicians. The final fitted segmentation model shows robustness and gives high mean performance rates [(AUC:.9426 (SD.0563); accuracy:.8777 (SD.0799); F-score: 0.7389 (SD.1550); Cohen’s kappa:.6585 (SD.1787)] when segmenting significant wound areas that include healing tissues. © 2015, International Federation for Medical and Biological Engineering. Source


Navas M.,University of Malaga | Luque-Baena R.M.,University of Malaga | Morente L.,Escuela Universitaria de Enfermeria | Coronado D.,Wimasis SL | And 2 more authors.
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | Year: 2013

Pressure ulcer is a clinical pathology of localized damage to the skin and underlying tissue caused by pressure, shear or friction. Diagnosis, care and treatment of pressure ulcers can result in extremely expensive costs for health systems. A reliable diagnosis supported by precise wound evaluation is crucial in order to success on the treatment decision and, in some cases, to save the patient's life. However, current evaluation procedures, focused mainly on visual inspection, do not seem to be accurate enough to accomplish this important task. This paper presents a computer-vision approach based on image processing algorithms and supervised learning techniques to help detecting and classifying wound tissue types which play an important role in wound diagnosis. The system proposed involves the use of the k-means clustering algorithm for image segmentation and a standard multilayer perceptron neural network to classify effectively each segmented region as the appropriate tissue type. Results obtained show a high performance rate which enables to support ulcer diagnosis by a reliable computational system. © 2013 Springer-Verlag Berlin Heidelberg. Source


Goberna Iglesias M.J.,Enfermeria | Mayo Moldes M.,Servicio de Anestesiologia | Lojo Vicente V.,Escuela Universitaria de Enfermeria
Revista de la Sociedad Espanola del Dolor | Year: 2014

It's well known and supported by various studies, that certain nursing performances are decisive to achieve aims related to quality, safety, effectiveness and efficient in health. The promotion and training in patient self-care is essential in order to pain autonomy that's undoubtedly among the key elements to achieve those objectives. Nowadays, we can't think about patient's health education without pay attention to enhance their autonomy which implies not only improve their care but also a better self-perception, and this will permit reduce the demand for care so important in the current socioeconomic situation. Source


Mallen-Perez L.,Hospital Clinic i Provincial de Barcelona | Juve-Udina M.,Escuela Universitaria de Enfermeria | Roe-Justiniano M.T.,Hospital Clinic i Provincial de Barcelona | Domenech-Farrarons A.,Hospital Clinic i Provincial de Barcelona
Matronas Profesion | Year: 2015

Objective: To explore the concept "labour pain" by analyzing available scientific literature, to identify its essential components. Method: The procedure used is the Wilsonian technique as described by Avant. This systematized method of conceptual analysis includes 11 steps: from the posed research questions to the establishment of results in language. Results: The concept "labour pain" is featured as a multidimensional, unique experience in physiological response to organic stimuli, influenced by internal and external factors and with several essential elements that delineate it and differentiate it from other types of pain. Conclusions: The findings from this study clarify the meaning of the concept and disseminate the knowledge and use of concept development and analysis procedures among midwives in our country. ©2015 Ediciones Mayo, S.A. All rights reserved. Source

Discover hidden collaborations