Programación funcional: conceptos y perspectivas

El presente artículo muestra conceptos subyacentes a la programación funcional, así como características que los hacen un enfoque particular y novedoso de la programación que lo convierten en una clara opción frente al enfoque imperativo convencional en el área del desarrollo de software

Una revisión sistemática de métodos de aprendizaje profundo aplicados a imágenes oculares

Artificial intelligence is having an important effect on different areas of medicine, and ophthalmology has not been the exception. In particular, deep learning methods have been applied successfully to the detection of clinical signs and the classification of ocular diseases. This represents a great potential to increase the number of people correctly diagnosed. In ophthalmology, deep learning methods have primarily been applied to eye fundus images and optical coherence tomography. On the one hand, these methods have achieved an outstanding performance in the detection of ocular diseases such as: diabetic retinopathy, glaucoma, diabetic macular degeneration and age-related macular degeneration.  On the other hand, several worldwide challenges have shared big eye imaging datasets with segmentation of part of the eyes, clinical signs and the ocular diagnostic performed by experts. In addition, these methods are breaking the stigma of black-box models, with the delivering of interpretable clinically information. This review provides an overview of the state-of-the-art deep learning methods used in ophthalmic images, databases and potential challenges for ocular diagnosisLa inteligencia artificial está teniendo un importante impacto en diversas áreas de la medicina y a la oftalmología no ha sido la excepción. En particular, los métodos de aprendizaje profundo han sido aplicados con éxito en la detección de signos clínicos y la clasificación de enfermedades oculares. Esto representa un potencial impacto en el incremento de pacientes correctamente y oportunamente diagnosticados. En oftalmología, los métodos de aprendizaje profundo se han aplicado principalmente a imágenes de fondo de ojo y tomografía de coherencia óptica. Por un lado, estos métodos han logrado un rendimiento sobresaliente en la detección de enfermedades oculares tales como: retinopatía diabética, glaucoma, degeneración macular diabética y degeneración macular relacionada con la edad. Por otro lado, varios desafíos mundiales han compartido grandes conjuntos de datos con segmentación de parte de los ojos, signos clínicos y el diagnóstico ocular realizado por expertos. Adicionalmente, estos métodos están rompiendo el estigma de los modelos de caja negra, con la entrega de información clínica interpretable. Esta revisión proporciona una visión general de los métodos de aprendizaje profundo de última generación utilizados en imágenes oftálmicas, bases de datos y posibles desafíos para los diagnósticos oculare

Characterization of electricity demand based on energy consumption data from Colombia

The development of dynamic energy distribution grids to optimize energy resources has become very important at the international level in recent years. A very important step in this development is to be able to characterize the population based on their consumption behaviour. However, traditional consumption meters that report information at a monthly rate provide little information for in-depth analysis. In Colombia, this has changed in recent years due to the implementation and integration of advanced metering infrastructure (AMI). This infrastructure allows to record consumption values in small time intervals, and the available data then allows for the execution of many analysis mechanisms. In this paper we present an analysis of the electricity demand profile from a new dataset of energy consumption in Colombia. A characterization of the users demand profiles is presented using a k-means clustering procedure. Whit this customer segmentation technique we show that is possible identify customer consumption patterns and to identify anomalies in the system. In addition, this type of analysis also allows to assess changes in the consumption pattern of users due to social measures such as those resulting from the coronavirus disease (COVID-19) pandemic

Recomendación de productos a partir de perfiles de usuario interpretables

Recommender systems allow users to have a personalized view of large sets of products, relieving the overload problem of choice in e-commerce sites. Usually, recommendations are obtained using the technique called "collaborative filtering". This technique filters the products the users wish, from those they don´t want, inferring affinities between products and users in a space of abstract features, also called a latent space. These techniques have proven to be of great predictive value, but these created profiles are neither understandable, nor editable for users, enclosing users in a bubble, in which they only receive collaborative recommendations conditioned by their historical behaviors. In our work we propose a method to build user profiles, defined in interpretable spaces, or defined in terms of collaborative tags or keywords (i.e. words extracted from the descriptions of the product), which can be interpreted and modified by users. The model proposed generate linear profiles, whose coefficients, positive or negative, reflect the user's affinity towards tags or keywords, according to the space selected. To test our hypothesis, we used the dataset of research in movie recommender systems from the University of Minnesota: Movielens. The results show that the predictive ability of the model, based on interpretable user profiles, is comparable to those models based on abstract profiles with the added benefit that these profiles are interpretable.Los sistemas de recomendación automática de productos permiten que los usuarios tengan una visión personalizada de grandes conjuntos de productos, lo cual alivia el problema de la sobrecarga de opciones en los sitios de comercio electrónico. Usualmente las recomendaciones se obtienen usando la técnica denominada “filtrado colaborativo”. Esta técnica permite filtrar los productos que el usuario desea de aquellos que no desea, infiriendo las afinidades entre productos, y usuarios, en un espacio de características abstracto. Si bien estas técnicas han mostrado ser de gran valor predictivo, su baja (o nula) interpretabilidad hace que el usuario, al no poder modificar su perfil, quede encerrado en una especie de burbuja, en la cual solo recibe recomendaciones colaborativas condicionadas por su comportamiento histórico. En este trabajo proponemos construir perfiles de usuario definidos en espacios interpretables como el de las etiquetas colaborativas (tags) o bien palabras claves extractadas automáticamente de las descripciones de los productos, que al ser interpretables permitan al usuario modificar su propio perfil. Este modelo se basa en la obtención de perfiles usando modelos lineales, cuyos coeficientes, positivos o negativos, reflejan la afinidad del usuario hacia la etiqueta o a la palabra clave. Para probar nuestra hipótesis, utilizamos el conjunto de datos de investigación en sistemas de recomendación de películas de la Universidad de Minnesota, MovieLens; los resultados obtenidos muestran que la capacidad predictiva del modelo es comparable a la de los métodos no interpretables, con el beneficio adicional de la interpretabilidad

Colombian consensus recommendations for diagnosis, management and treatment of the infection by SARS-COV-2/ COVID-19 in health care facilities - Recommendations from expert´s group based and informed on evidence

La Asociación Colombiana de Infectología (ACIN) y el Instituto de Evaluación de Nuevas Tecnologías de la Salud (IETS) conformó un grupo de trabajo para desarrollar recomendaciones informadas y basadas en evidencia, por consenso de expertos para la atención, diagnóstico y manejo de casos de Covid 19. Estas guías son dirigidas al personal de salud y buscar dar recomendaciones en los ámbitos de la atención en salud de los casos de Covid-19, en el contexto nacional de Colombia

Mortality from gastrointestinal congenital anomalies at 264 hospitals in 74 low-income, middle-income, and high-income countries: a multicentre, international, prospective cohort study

Summary Background Congenital anomalies are the fifth leading cause of mortality in children younger than 5 years globally. Many gastrointestinal congenital anomalies are fatal without timely access to neonatal surgical care, but few studies have been done on these conditions in low-income and middle-income countries (LMICs). We compared outcomes of the seven most common gastrointestinal congenital anomalies in low-income, middle-income, and high-income countries globally, and identified factors associated with mortality. Methods We did a multicentre, international prospective cohort study of patients younger than 16 years, presenting to hospital for the first time with oesophageal atresia, congenital diaphragmatic hernia, intestinal atresia, gastroschisis, exomphalos, anorectal malformation, and Hirschsprung’s disease. Recruitment was of consecutive patients for a minimum of 1 month between October, 2018, and April, 2019. We collected data on patient demographics, clinical status, interventions, and outcomes using the REDCap platform. Patients were followed up for 30 days after primary intervention, or 30 days after admission if they did not receive an intervention. The primary outcome was all-cause, in-hospital mortality for all conditions combined and each condition individually, stratified by country income status. We did a complete case analysis. Findings We included 3849 patients with 3975 study conditions (560 with oesophageal atresia, 448 with congenital diaphragmatic hernia, 681 with intestinal atresia, 453 with gastroschisis, 325 with exomphalos, 991 with anorectal malformation, and 517 with Hirschsprung’s disease) from 264 hospitals (89 in high-income countries, 166 in middleincome countries, and nine in low-income countries) in 74 countries. Of the 3849 patients, 2231 (58·0%) were male. Median gestational age at birth was 38 weeks (IQR 36–39) and median bodyweight at presentation was 2·8 kg (2·3–3·3). Mortality among all patients was 37 (39·8%) of 93 in low-income countries, 583 (20·4%) of 2860 in middle-income countries, and 50 (5·6%) of 896 in high-income countries (p<0·0001 between all country income groups). Gastroschisis had the greatest difference in mortality between country income strata (nine [90·0%] of ten in lowincome countries, 97 [31·9%] of 304 in middle-income countries, and two [1·4%] of 139 in high-income countries; p≤0·0001 between all country income groups). Factors significantly associated with higher mortality for all patients combined included country income status (low-income vs high-income countries, risk ratio 2·78 [95% CI 1·88–4·11], p<0·0001; middle-income vs high-income countries, 2·11 [1·59–2·79], p<0·0001), sepsis at presentation (1·20 [1·04–1·40], p=0·016), higher American Society of Anesthesiologists (ASA) score at primary intervention (ASA 4–5 vs ASA 1–2, 1·82 [1·40–2·35], p<0·0001; ASA 3 vs ASA 1–2, 1·58, [1·30–1·92], p<0·0001]), surgical safety checklist not used (1·39 [1·02–1·90], p=0·035), and ventilation or parenteral nutrition unavailable when needed (ventilation 1·96, [1·41–2·71], p=0·0001; parenteral nutrition 1·35, [1·05–1·74], p=0·018). Administration of parenteral nutrition (0·61, [0·47–0·79], p=0·0002) and use of a peripherally inserted central catheter (0·65 [0·50–0·86], p=0·0024) or percutaneous central line (0·69 [0·48–1·00], p=0·049) were associated with lower mortality. Interpretation Unacceptable differences in mortality exist for gastrointestinal congenital anomalies between lowincome, middle-income, and high-income countries. Improving access to quality neonatal surgical care in LMICs will be vital to achieve Sustainable Development Goal 3.2 of ending preventable deaths in neonates and children younger than 5 years by 2030

Programación funcional: conceptos y perspectivas

El presente artículo muestra conceptos subyacentes a la programación funcional, así como características que los hacen un enfoque particular y novedoso de la programación que lo convierten en una clara opción frente al enfoque imperativo convencional en el área del desarrollo de software

Programación funcional: conceptos y perspectivas

El presente artículo muestra conceptos subyacentes a la programación funcional, así como características que los hacen un enfoque particular y novedoso de la programación que lo convierten en una clara opción frente al enfoque imperativo convencional en el área del desarrollo de software

Towards a kernelbased model for artificial immune networks

This paper presents an adaptation to the strategy of kernel methods of a wellkn own artificial immune network. This adaptation brings to artificial immune systems, for the first time, some of the advantages of kernel methods, such as the ability to deal with non-vector data and the mapping to high-dimensional spaces through the kernel trick. Preliminar y experiments were carried out in order to get some insights of the behavior of the proposed model

An ontology-based information extractor for data-rich documents in the information technology domain

This paper presents an information extraction method, suitable for data-rich documents, based on the knowledge represented in a domain ontology. The extractor combines a fuzzy string matcher and a word sense disambiguation (WSD) algorithm. The fuzzy string matcher finds mentions of terms combining character-level and token-level similarity measures dealing with non-standardized acronyms and inconsistent abbreviation styles. We propose a new character-level edit distance sensitive to prefixes called root distance and a token-level similarity algorithm for fuzzy acronym detection. Additionally, a WSD strategy using an ontology-based semantic relatedness measure is used to solve the inherent ambiguity of some entities. The WSD module finds a sense combination over all the document length optimizing the document semantic coherence. Our approach seems to be suitable to extract information from data-rich documents describing Orly one main object (i.e. product) by document. The results showed a precision of 78.9% with 99.5% recall using documents and an ontology related to laptop computers domain