Scene understanding for autonomous robots operating in indoor environments

Mención Internacional en el título de doctorThe idea of having robots among us is not new. Great efforts are continually made to replicate human intelligence, with the vision of having robots performing different activities, including hazardous, repetitive, and tedious tasks. Research has demonstrated that robots are good at many tasks that are hard for us, mainly in terms of precision, efficiency, and speed. However, there are some tasks that humans do without much effort that are challenging for robots. Especially robots in domestic environments are far from satisfactorily fulfilling some tasks, mainly because these environments are unstructured, cluttered, and with a variety of environmental conditions to control. This thesis addresses the problem of scene understanding in the context of autonomous robots operating in everyday human environments. Furthermore, this thesis is developed under the HEROITEA research project that aims to develop a robot system to help elderly people in domestic environments as an assistant. Our main objective is to develop different methods that allow robots to acquire more information from the environment to progressively build knowledge that allows them to improve the performance on high-level robotic tasks. In this way, scene understanding is a broad research topic, and it is considered a complex task due to the multiple sub-tasks that are involved. In that context, in this thesis, we focus on three sub-tasks: object detection, scene recognition, and semantic segmentation of the environment. Firstly, we implement methods to recognize objects considering real indoor environments. We applied machine learning techniques incorporating uncertainties and more modern techniques based on deep learning. Besides, apart from detecting objects, it is essential to comprehend the scene where they can occur. For this reason, we propose an approach for scene recognition that considers the influence of the detected objects in the prediction process. We demonstrate that the exiting objects and their relationships can improve the inference about the scene class. We also consider that a scene recognition model can benefit from the advantages of other models. We propose a multi-classifier model for scene recognition based on weighted voting schemes. The experiments carried out in real-world indoor environments demonstrate that the adequate combination of independent classifiers allows obtaining a more robust and precise model for scene recognition. Moreover, to increase the understanding of a robot about its surroundings, we propose a new division of the environment based on regions to build a useful representation of the environment. Object and scene information is integrated into a probabilistic fashion generating a semantic map of the environment containing meaningful regions within each room. The proposed system has been assessed on simulated and real-world domestic scenarios, demonstrating its ability to generate consistent environment representations. Lastly, full knowledge of the environment can enhance more complex robotic tasks; that is why in this thesis, we try to study how a complete knowledge of the environment influences the robot’s performance in high-level tasks. To do so, we select an essential task, which is searching for objects. This mundane task can be considered a precondition to perform many complex robotic tasks such as fetching and carrying, manipulation, user requirements, among others. The execution of these activities by service robots needs full knowledge of the environment to perform each task efficiently. In this thesis, we propose two searching strategies that consider prior information, semantic representation of the environment, and the relationships between known objects and the type of scene. All our developments are evaluated in simulated and real-world environments, integrated with other systems, and operating in real platforms, demonstrating their feasibility to implement in real scenarios, and in some cases outperforming other approaches. We also demonstrate how our representation of the environment can boost the performance of more complex robotic tasks compared to more standard environmental representations.La idea de tener robots entre nosotros no es nueva. Continuamente se realizan grandes esfuerzos para replicar la inteligencia humana, con la visión de tener robots que realicen diferentes actividades, incluidas tareas peligrosas, repetitivas y tediosas. La investigación ha demostrado que los robots son buenos en muchas tareas que resultan difíciles para nosotros, principalmente en términos de precisión, eficiencia y velocidad. Sin embargo, existen tareas que los humanos realizamos sin mucho esfuerzo y que son un desafío para los robots. Especialmente, los robots en entornos domésticos están lejos de cumplir satisfactoriamente algunas tareas, principalmente porque estos entornos no son estructurados, pueden estar desordenados y cuentan con una gran variedad de condiciones ambientales que controlar. Esta tesis aborda el problema de la comprensión de la escena en el contexto de robots autónomos que operan en entornos humanos cotidianos. Asimismo, esta tesis se desarrolla en el marco del proyecto de investigación HEROITEA que tiene como objetivo desarrollar un sistema robótico que funcione como asistente para ayudar a personas mayores en entornos domésticos. Nuestro principal objetivo es desarrollar diferentes métodos que permitan a los robots adquirir más información del entorno a fin de construir progresivamente un conocimiento que les permita mejorar su desempeño en tareas robóticas más complejas. En este sentido, la comprensión de escenas es un tema de investigación amplio, y se considera una tarea compleja debido a las múltiples subtareas involucradas. En esta tesis nos enfocamos específicamente en tres subtareas: detección de objetos, reconocimiento de escenas y etiquetado semántico del entorno. Por un lado, implementamos métodos para el reconocimiento de objectos considerando entornos interiores reales. Aplicamos técnicas de aprendizaje automático incorporando incertidumbres y técnicas más modernas basadas en aprendizaje profundo. Además, aparte de detectar objetos, es fundamental comprender la escena donde estos se encuentran. Por esta razón, proponemos un modelo para el reconocimiento de escenas que considera la influencia de los objetos detectados en el proceso de predicción. Demostramos que los objetos existentes y sus relaciones pueden mejorar el proceso de inferencia de la categoría de la escena. También consideramos que un modelo de reconocimiento de escenas puede beneficiarse de las ventajas de otros modelos. Por ello, proponemos un multiclasificador para el reconocimiento de escenas basado en esquemas de votación ponderados. Los experimentos llevados a cabo en entornos interiores reales demuestran que la combinación adecuada de clasificadores independientes permite obtener un modelo más robusto y preciso para el reconocimiento de escenas. Adicionalmente, para aumentar la comprensión de un robot acerca de su entorno, proponemos una nueva división del entorno basada en regiones a fin de construir una representación útil del entorno. La información de objetos y de la escena se integra de forma probabilística generando un mapa semántico que contiene regiones significativas dentro de cada habitación. El sistema propuesto ha sido evaluado en entornos domésticos simulados y reales, demostrando su capacidad para generar representaciones consistentes del entorno. Por otro lado, el conocimiento integral del entorno puede mejorar tareas robóticas más complejas; es por ello que en esta tesis analizamos cómo el conocimiento completo del entorno influye en el desempeño del robot en tareas de alto nivel. Para ello, seleccionamos una tarea fundamental, que es la búsqueda de objetos. Esta tarea mundana puede considerarse una condición previa para realizar diversas tareas robóticas complejas, como transportar objetos, tareas de manipulación, atender requerimientos del usuario, entre otras. La ejecución de estas actividades por parte de robots de servicio requiere un conocimiento profundo del entorno para realizar cada tarea de manera eficiente. En esta tesis proponemos dos estrategias de búsqueda de objetos que consideran información previa, la representación semántica del entorno, las relaciones entre los objetos conocidos y el tipo de escena. Todos nuestros desarrollos son evaluados en entornos simulados y reales, integrados con otros sistemas y operando en plataformas reales, demostrando su viabilidad de ser implementados en escenarios reales y, en algunos casos, superando a otros enfoques. También demostramos cómo nuestra representación del entorno puede mejorar el desempeño de tareas robóticas más complejas en comparación con representaciones del entorno más tradicionales.Programa de Doctorado en Ingeniería Eléctrica, Electrónica y Automática por la Universidad Carlos III de MadridPresidente: Carlos Balaguer Bernaldo de Quirós.- Secretario: Fernando Matía Espada.- Vocal: Klaus Strob

Object detection applied to indoor environments for mobile robot navigation

To move around the environment, human beings depend on sight more than their other senses, because it provides information about the size, shape, color and position of an object. The increasing interest in building autonomous mobile systems makes the detection and recognition of objects in indoor environments a very important and challenging task. In this work, a vision system to detect objects considering usual human environments, able to work on a real mobile robot, is developed. In the proposed system, the classification method used is Support Vector Machine (SVM) and as input to this system, RGB and depth images are used. Different segmentation techniques have been applied to each kind of object. Similarly, two alternatives to extract features of the objects are explored, based on geometric shape descriptors and bag of words. The experimental results have demonstrated the usefulness of the system for the detection and location of the objects in indoor environments. Furthermore, through the comparison of two proposed methods for extracting features, it has been determined which alternative offers better performance. The final results have been obtained taking into account the proposed problem and that the environment has not been changed, that is to say, the environment has not been altered to perform the tests.The research leading to these results has received funding from the RoboCity2030-III-CM project (Robótica aplicada a la mejora de la calidad de vida de los ciudadanos, fase III; S2013/MIT-2748), funded by Programas de Actividades I+D en la Comunidad de Madrid and co-funded by Structural Funds of the EU and NAVEGASE-AUTOCOGNAV project (DPI2014-53525-C3-3-R), funded by Ministerio de Economía y competitividad of Spain

A Semantic Labeling of the Environment Based on What People Do

In this work, a system is developed for semantic labeling of locations based on what people do. This system is useful for semantic navigation of mobile robots. The system differentiates environments according to what people do in them. Background sound, number of people in a room and amount of movement of those people are items to be considered when trying to tell if people are doing different actions. These data are sampled, and it is assumed that people behave differently and perform different actions. A support vector machine is trained with the obtained samples, and therefore, it allows one to identify the room. Finally, the results are discussed and support the hypothesis that the proposed system can help to semantically label a room.The research leading to these results has received funding from the RoboCity2030-III-CMproject (Robótica aplicada a la mejora de la calidad de vida de los ciudadanos. fase III; S2013/MIT-2748), funded by Programas de Actividades I+Den la Comunidad de Madrid and cofunded by Structural Funds of the EU and NAVEGASEAUTOCOGNAVproject (DPI2014-53525-C3-3-R), funded by Ministerio de Economía y Competitividad of Spain.The research leading to these results has received funding from the RoboCity2030-III-CMproject (Robótica aplicada a la mejora de la calidad de vida de los ciudadanos. fase III; S2013/MIT-2748), funded by Programas de Actividades I+Den la Comunidad de Madrid and cofunded by Structural Funds of the EU and NAVEGASEAUTOCOGNAVproject (DPI2014-53525-C3-3-R), funded by Ministerio de Economía y Competitividad of Spain

Trato humanizado en el cuidado de enfermería Hospital Universitario San Jorge de Pereira 2002 ,

CD-T 610.730 699 H427;174 p.Identificar las características del trato entre el personal de enfermería y sus pacientes en los servicios de atención al adulto del Hospital Universitario San Jorge, y analizarlas en su contexto para abordar un conocimiento quepermita modificar actitudes, políticas administrativas y de formación del (la) enfermero (a)Universidad Libre de Pereir

First Latin American clinical practice guidelines for the treatment of systemic lupus erythematosus: Latin American Group for the Study of Lupus (GLADEL, Grupo Latino Americano de Estudio del Lupus)-Pan-American League of Associations of Rheumatology (PANLAR)

Systemic lupus erythematosus (SLE), a complex and heterogeneous autoimmune disease, represents a significant challenge for both diagnosis and treatment. Patients with SLE in Latin America face special problems that should be considered when therapeutic guidelines are developed. The objective of the study is to develop clinical practice guidelines for Latin American patients with lupus. Two independent teams (rheumatologists with experience in lupus management and methodologists) had an initial meeting in Panama City, Panama, in April 2016. They selected a list of questions for the clinical problems most commonly seen in Latin American patients with SLE. These were addressed with the best available evidence and summarised in a standardised format following the Grading of Recommendations Assessment, Development and Evaluation approach. All preliminary findings were discussed in a second face-to-face meeting in Washington, DC, in November 2016. As a result, nine organ/system sections are presented with the main findings; an 'overarching' treatment approach was added. Special emphasis was made on regional implementation issues. Best pharmacologic options were examined for musculoskeletal, mucocutaneous, kidney, cardiac, pulmonary, neuropsychiatric, haematological manifestations and the antiphospholipid syndrome. The roles of main therapeutic options (ie, glucocorticoids, antimalarials, immunosuppressant agents, therapeutic plasma exchange, belimumab, rituximab, abatacept, low-dose aspirin and anticoagulants) were summarised in each section. In all cases, benefits and harms, certainty of the evidence, values and preferences, feasibility, acceptability and equity issues were considered to produce a recommendation with special focus on ethnic and socioeconomic aspects. Guidelines for Latin American patients with lupus have been developed and could be used in similar settings.Fil: Pons Estel, Bernardo A.. Centro Regional de Enfermedades Autoinmunes y Reumáticas; ArgentinaFil: Bonfa, Eloisa. Universidade de Sao Paulo; BrasilFil: Soriano, Enrique R.. Instituto Universitario Hospital Italiano de Buenos Aires. Rectorado.; ArgentinaFil: Cardiel, Mario H.. Centro de Investigación Clínica de Morelia; MéxicoFil: Izcovich, Ariel. Hospital Alemán; ArgentinaFil: Popoff, Federico. Hospital Aleman; ArgentinaFil: Criniti, Juan M.. Hospital Alemán; ArgentinaFil: Vásquez, Gloria. Universidad de Antioquia; ColombiaFil: Massardo, Loreto. Universidad San Sebastián; ChileFil: Duarte, Margarita. Hospital de Clínicas; ParaguayFil: Barile Fabris, Leonor A.. Hospital Angeles del Pedregal; MéxicoFil: García, Mercedes A.. Universidad de Buenos Aires. Facultad de Medicina. Hospital de Clínicas General San Martín; ArgentinaFil: Amigo, Mary Carmen. Centro Médico Abc; MéxicoFil: Espada, Graciela. Gobierno de la Ciudad de Buenos Aires. Hospital General de Niños "Ricardo Gutiérrez"; ArgentinaFil: Catoggio, Luis J.. Hospital Italiano. Instituto Universitario. Escuela de Medicina; ArgentinaFil: Sato, Emilia Inoue. Universidade Federal de Sao Paulo; BrasilFil: Levy, Roger A.. Universidade do Estado de Rio do Janeiro; BrasilFil: Acevedo Vásquez, Eduardo M.. Universidad Nacional Mayor de San Marcos; PerúFil: Chacón Díaz, Rosa. Policlínica Méndez Gimón; VenezuelaFil: Galarza Maldonado, Claudio M.. Corporación Médica Monte Sinaí; EcuadorFil: Iglesias Gamarra, Antonio J.. Universidad Nacional de Colombia; ColombiaFil: Molina, José Fernando. Centro Integral de Reumatología; ColombiaFil: Neira, Oscar. Universidad de Chile; ChileFil: Silva, Clóvis A.. Universidade de Sao Paulo; BrasilFil: Vargas Peña, Andrea. Hospital Pasteur Montevideo; UruguayFil: Gómez Puerta, José A.. Hospital Clinic Barcelona; EspañaFil: Scolnik, Marina. Instituto Universitario Hospital Italiano de Buenos Aires. Rectorado.; ArgentinaFil: Pons Estel, Guillermo J.. Centro Regional de Enfermedades Autoinmunes y Reumáticas; Argentina. Hospital Provincial de Rosario; ArgentinaFil: Ugolini Lopes, Michelle R.. Universidade de Sao Paulo; BrasilFil: Savio, Verónica. Instituto Universitario Hospital Italiano de Buenos Aires. Rectorado.; ArgentinaFil: Drenkard, Cristina. University of Emory; Estados UnidosFil: Alvarellos, Alejandro J.. Hospital Privado Universitario de Córdoba; ArgentinaFil: Ugarte Gil, Manuel F.. Universidad Cientifica del Sur; Perú. Hospital Nacional Guillermo Almenara Irigoyen; PerúFil: Babini, Alejandra. Instituto Universitario Hospital Italiano de Buenos Aires. Rectorado.; ArgentinaFil: Cavalcanti, André. Universidade Federal de Pernambuco; BrasilFil: Cardoso Linhares, Fernanda Athayde. Hospital Pasteur Montevideo; UruguayFil: Haye Salinas, Maria Jezabel. Hospital Privado Universitario de Córdoba; ArgentinaFil: Fuentes Silva, Yurilis J.. Universidad de Oriente - Núcleo Bolívar; VenezuelaFil: Montandon De Oliveira E Silva, Ana Carolina. Universidade Federal de Goiás; BrasilFil: Eraso Garnica, Ruth M.. Universidad de Antioquia; ColombiaFil: Herrera Uribe, Sebastián. Hospital General de Medellin Luz Castro de Gutiérrez; ColombiaFil: Gómez Martín, DIana. Instituto Nacional de la Nutrición Salvador Zubiran; MéxicoFil: Robaina Sevrini, Ricardo. Universidad de la República; UruguayFil: Quintana, Rosana M.. Hospital Provincial de Rosario; Argentina. Centro Regional de Enfermedades Autoinmunes y Reumáticas; ArgentinaFil: Gordon, Sergio. Hospital Interzonal General de Agudos Dr Oscar Alende. Unidad de Reumatología y Enfermedades Autoinmunes Sistémicas; ArgentinaFil: Fragoso Loyo, Hilda. Instituto Nacional de la Nutrición Salvador Zubiran; MéxicoFil: Rosario, Violeta. Hospital Docente Padre Billini; República DominicanaFil: Saurit, Verónica. Hospital Privado Universitario de Córdoba; ArgentinaFil: Appenzeller, Simone. Universidade Estadual de Campinas; BrasilFil: Dos Reis Neto, Edgard Torres. Universidade Federal de Sao Paulo; BrasilFil: Cieza, Jorge. Hospital Nacional Edgardo Rebagliati Martins; PerúFil: González Naranjo, Luis A.. Universidad de Antioquia; ColombiaFil: González Bello, Yelitza C.. Ceibac; MéxicoFil: Collado, María Victoria. Universidad de Buenos Aires. Facultad de Medicina. Instituto de Investigaciones Médicas; ArgentinaFil: Sarano, Judith. Universidad de Buenos Aires. Facultad de Medicina. Instituto de Investigaciones Médicas; ArgentinaFil: Retamozo, Maria Soledad. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba. Instituto de Investigaciones en Ciencias de la Salud. Universidad Nacional de Córdoba. Instituto de Investigaciones en Ciencias de la Salud; ArgentinaFil: Sattler, María E.. Provincia de Buenos Aires. Ministerio de Salud. Hospital Interzonal de Agudos "Eva Perón"; ArgentinaFil: Gamboa Cárdenas, Rocio V.. Hospital Nacional Guillermo Almenara Irigoyen; PerúFil: Cairoli, Ernesto. Universidad de la República; UruguayFil: Conti, Silvana M.. Hospital Provincial de Rosario; ArgentinaFil: Amezcua Guerra, Luis M.. Instituto Nacional de Cardiologia Ignacio Chavez; MéxicoFil: Silveira, Luis H.. Instituto Nacional de Cardiologia Ignacio Chavez; MéxicoFil: Borba, Eduardo F.. Universidade de Sao Paulo; BrasilFil: Pera, Mariana A.. Hospital Interzonal General de Agudos General San Martín; ArgentinaFil: Alba Moreyra, Paula B.. Universidad Nacional de Córdoba. Facultad de Medicina; ArgentinaFil: Arturi, Valeria. Hospital Interzonal General de Agudos General San Martín; ArgentinaFil: Berbotto, Guillermo A.. Provincia de Buenos Aires. Ministerio de Salud. Hospital Interzonal de Agudos "Eva Perón"; ArgentinaFil: Gerling, Cristian. Hospital Interzonal General de Agudos Dr Oscar Alende. Unidad de Reumatología y Enfermedades Autoinmunes Sistémicas; ArgentinaFil: Gobbi, Carla Andrea. Universidad Nacional de Córdoba. Facultad de Medicina; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Gervasoni, Viviana L.. Hospital Provincial de Rosario; ArgentinaFil: Scherbarth, Hugo R.. Hospital Interzonal General de Agudos Dr Oscar Alende. Unidad de Reumatología y Enfermedades Autoinmunes Sistémicas; ArgentinaFil: Brenol, João C. Tavares. Hospital de Clinicas de Porto Alegre; BrasilFil: Cavalcanti, Fernando. Universidade Federal de Pernambuco; BrasilFil: Costallat, Lilian T. Lavras. Universidade Estadual de Campinas; BrasilFil: Da Silva, Nilzio A.. Universidade Federal de Goiás; BrasilFil: Monticielo, Odirlei A.. Hospital de Clinicas de Porto Alegre; BrasilFil: Seguro, Luciana Parente Costa. Universidade de Sao Paulo; BrasilFil: Xavier, Ricardo M.. Hospital de Clinicas de Porto Alegre; BrasilFil: Llanos, Carolina. Universidad Católica de Chile; ChileFil: Montúfar Guardado, Rubén A.. Instituto Salvadoreño de la Seguridad Social; El SalvadorFil: Garcia De La Torre, Ignacio. Hospital General de Occidente; MéxicoFil: Pineda, Carlos. Instituto Nacional de Rehabilitación; MéxicoFil: Portela Hernández, Margarita. Umae Hospital de Especialidades Centro Medico Nacional Siglo Xxi; MéxicoFil: Danza, Alvaro. Hospital Pasteur Montevideo; UruguayFil: Guibert Toledano, Marlene. Medical-surgical Research Center; CubaFil: Reyes, Gil Llerena. Medical-surgical Research Center; CubaFil: Acosta Colman, Maria Isabel. Hospital de Clínicas; ParaguayFil: Aquino, Alicia M.. Hospital de Clínicas; ParaguayFil: Mora Trujillo, Claudia S.. Hospital Nacional Edgardo Rebagliati Martins; PerúFil: Muñoz Louis, Roberto. Hospital Docente Padre Billini; República DominicanaFil: García Valladares, Ignacio. Centro de Estudios de Investigación Básica y Clínica; MéxicoFil: Orozco, María Celeste. Instituto de Rehabilitación Psicofísica; ArgentinaFil: Burgos, Paula I.. Pontificia Universidad Católica de Chile; ChileFil: Betancur, Graciela V.. Instituto de Rehabilitación Psicofísica; ArgentinaFil: Alarcón, Graciela S.. Universidad Peruana Cayetano Heredia; Perú. University of Alabama at Birmingahm; Estados Unido

Impact of COVID-19 on cardiovascular testing in the United States versus the rest of the world

Objectives: This study sought to quantify and compare the decline in volumes of cardiovascular procedures between the United States and non-US institutions during the early phase of the coronavirus disease-2019 (COVID-19) pandemic. Background: The COVID-19 pandemic has disrupted the care of many non-COVID-19 illnesses. Reductions in diagnostic cardiovascular testing around the world have led to concerns over the implications of reduced testing for cardiovascular disease (CVD) morbidity and mortality. Methods: Data were submitted to the INCAPS-COVID (International Atomic Energy Agency Non-Invasive Cardiology Protocols Study of COVID-19), a multinational registry comprising 909 institutions in 108 countries (including 155 facilities in 40 U.S. states), assessing the impact of the COVID-19 pandemic on volumes of diagnostic cardiovascular procedures. Data were obtained for April 2020 and compared with volumes of baseline procedures from March 2019. We compared laboratory characteristics, practices, and procedure volumes between U.S. and non-U.S. facilities and between U.S. geographic regions and identified factors associated with volume reduction in the United States. Results: Reductions in the volumes of procedures in the United States were similar to those in non-U.S. facilities (68% vs. 63%, respectively; p = 0.237), although U.S. facilities reported greater reductions in invasive coronary angiography (69% vs. 53%, respectively; p < 0.001). Significantly more U.S. facilities reported increased use of telehealth and patient screening measures than non-U.S. facilities, such as temperature checks, symptom screenings, and COVID-19 testing. Reductions in volumes of procedures differed between U.S. regions, with larger declines observed in the Northeast (76%) and Midwest (74%) than in the South (62%) and West (44%). Prevalence of COVID-19, staff redeployments, outpatient centers, and urban centers were associated with greater reductions in volume in U.S. facilities in a multivariable analysis. Conclusions: We observed marked reductions in U.S. cardiovascular testing in the early phase of the pandemic and significant variability between U.S. regions. The association between reductions of volumes and COVID-19 prevalence in the United States highlighted the need for proactive efforts to maintain access to cardiovascular testing in areas most affected by outbreaks of COVID-19 infection

Gestión del conocimiento: perspectiva multidisciplinaria. Volumen 11

El libro “Gestión del Conocimiento. Perspectiva Multidisciplinaria”, Volumen 11, de la Colección Unión Global, es resultado de investigaciones. Los capítulos del libro, son resultados de investigaciones desarrolladas por sus autores. El libro cuenta con el apoyo de los grupos de investigación: Universidad Sur del Lago “Jesús María Semprúm” (UNESUR), Zulia – Venezuela; Universidad Politécnica Territorial de Falcón Alonso Gamero (UPTAG), Falcón – Venezuela; Universidad Politécnica Territorial de Mérida Kleber Ramírez (UPTM), Mérida – Venezuela; Universidad Guanajuato (UG) - Campus Celaya - Salvatierra - Cuerpo Académico de Biodesarrollo y Bioeconomía en las Organizaciones y Políticas Públicas (C.A.B.B.O.P.P), Guanajuato – México; Centro de Altos Estudios de Venezuela (CEALEVE), Zulia – Venezuela, Centro Integral de Formación Educativa Especializada del Sur (CIFE - SUR) - Zulia - Venezuela, Centro de Investigaciones Internacionales SAS (CIN), Antioquia - Colombia.y diferentes grupos de investigación del ámbito nacional e internacional que hoy se unen para estrechar vínculos investigativos, para que sus aportes científicos formen parte de los libros que se publiquen en formatos digital e impreso

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