Search

JCR Web of Science; Year: 2020 in Categories: Public, Environmental & Occupational Health: Q1, Current Impact Factor: 3.390, 5-year Impact Factor: 3.789., Background: treating infectious diseases in elderly individuals is difficult; patient referral to emergency services often occurs, since the elderly tend to arrive at consultations with advanced, serious symptoms. Aim: it was hypothesized that anticipating an infectious disease diagnosis by a few days could significantly improve a patient?s well-being and reduce the burden on emergency health system services. Methods: vital signs from residents were taken daily and transferred to a database in the cloud. Classifiers were used to recognize patterns in the spatial domain process of the collected data. Doctors reported their diagnoses when any disease presented. A flexible microservice architecture provided access and functionality to the system. Results: combining two different domains, health and technology, is not easy, but the results are encouraging. The classifiers reported good results; the system has been well accepted by medical personnel and is proving to be cost-effective and a good solution to service disadvantaged areas. In this context, this research found the importance of certain clinical variables in the identification of infectious diseases. Conclusions: this work explores how to apply mobile communications, cloud services, and machine learning technology, in order to provide efficient tools for medical staff in nursing homes. The scalable architecture can be extended to big data applications that may extract valuable knowledge patterns for medical research., European Commission

The derelict Remance gold mine is a possible source of pollution with potentially toxic elements (PTEs). In the study area, diverse mine waste has been left behind and exposed to weather conditions, and poses risks for soil, plants and water bodies, and also for the health of local inhabitants. This study sought to perform an ecological and health risk assessment of derelict gold mining areas with incomplete remediation, including: (i) characterizing the geochemical distribution of PTEs, (ii) assessing ecological risk by estimating the pollution load index (PLI) and potential ecological risk index (RI), (iii) assessing soil health by dehydrogenase activity, and iv) establishing non-carcinogenic (HI) and carcinogenic risks (CR) for local inhabitants. Soil health seems to depend on not only PTE concentrations, but also on organic matter (OM). Both indexes (PLI and RI) ranged from high to extreme near mining and waste accumulation sites. As indicated by both the HI and CR results, the mining area poses a health risk for local inhabitants and particularly for children. For this reason, it will be necessary to set up environmental management programs in the areas that are most affected (tailings and surrounding areas) and accordingly establish the best remediation strategies to minimize risks for the local population.

This article describes the design, development and implementation of a set of microservices based on an architecture that enables detection and assisted clinical diagnosis within the field of infectious diseases of elderly patients, via a telemonitoring system. The proposed system is designed to continuously update a medical database fed with vital signs from biosensor kits applied by nurses to elderly people on a daily basis. The database is hosted in the cloud and is managed by a flexible microservices software architecture. The computational paradigms of the edge and the cloud were used in the implementation of a hybrid cloud architecture in order to support versatile high-performance applications under the microservices pattern for the pre-diagnosis of infectious diseases in elderly patients. The results of an analysis of the usability of the equipment, the performance of the architecture and the service concept show that the proposed e-health system is feasible and innovative. The system components are also selected to give a cost-effective implementation for people living in disadvantaged areas. The proposed e-health system is also suitable for distributed computing, big data and NoSQL structures, thus allowing the immediate application of machine learning and AI algorithms to discover knowledge patterns from the overall population., European Commission

Agents are endowed with time, which in turn is invested in projects that generate profit. A mechanism divides the profit generated by these agents depending on the allocation of time as well as the amount of profit made by every project. We study mechanisms that incentivize agents to contribute their time to a level that results in the maximal aggregate profit at the Nash equilibrium, regardless of the production functions involved (efficiency). Our main finding involves the characterization of all mechanisms that satisfy efficiency. Furthermore, within this class, we characterize the mechanisms that are monotone on the addition of time to agents as well as those monotone on the payoffs of the agents with respect to technological improvements in the generation of profit. The class of efficient mechanisms depends on the type of available projects and their connectedness. It expands earlier profit-sharing mechanisms that are independent of profit generation.

While stress has been implicated in functional dyspepsia (FD), the mechanisms by which stress results in symptoms are not well defined. The aim of the current study was to assess gastric myoelectric and autonomic changes in response to a physical stressor in youth with FD. In a group of healthy controls and pediatric FD subjects, we recorded ECG and EGG signals 30 min before and 60 min after, a cold pressor task (CPT). Gastric EGG and heart rate variability (HRV) parameters were calculated in pre- and post-CPT stages and in short intervals. The pre-CPT percent tachygastria was higher in FD subjects as compared to controls. However, CPT did not induce any EGG changes in either controls or FD subjects and the two groups did not differ from each other post-CPT. The CPT resulted in an increase in HRV and standard deviation of NN intervals in controls; there was no change in any HRV parameter in FD subjects. Acute physical stress does not appear to induce gastric electrical abnormalities in youth with FD. Youth with FD appear to lack the normal flexible autonomic response to a physical stressor.

Obesity and periodontitis are among the most common non-communicable diseases, and epidemiological studies report the influence of obesity in the onset and progression of periodontitis. Data indicate that increased body mass index, waist circumference, percentage of subcutaneous body fat, and serum lipid levels are associated with increased risk to develop periodontitis. The underlying biological mechanisms of this association involve adipose tissue-derived cytokines, such as tumour necrosis factor-α and interleukin-6, which affect whole-body metabolism and contribute to the development of a low-grade systemic inflammation. Multiple studies report a positive association between these two diseases across diverse populations. Obesity does not appear to impair the success of periodontal therapy. However, currently available evidence is variable and therefore inconclusive. Despite the limited evidence about recommendations on treatment planning, oral healthcare professionals need to be aware of the complexity of obesity to counsel their patients about the importance of maintaining healthy body weight and performing good oral hygiene procedures.

How users perceive password memorability is not well understood, despite passwords being studied thoroughly. We investigate the relationship between users’ perceptions of the memorability of a number of passwords and the users’ electroencephalogram (EEG) data collected using brain-computer interfaces (BCIs). In this study, we asked 75 participants to rank the comparative memorability of 15 blocks of 5 passwords each, while collecting their EEG data. Passwords had the same length but differed in characteristics and strength. We were able to find a consistency between how users perceive password memorability, the EEG signals elicited by the least and most memorable passwords from a list, and the actual measured strength of these passwords. Our results suggest that EEG signals could fill the gap in the absence of information about how users rank passwords, or password strength, with an 82% accuracy.

Forecasting the energy consumption of heating, ventilating, and air conditioning systems is important for the energy efficiency and sustainability of buildings. In fact, conventional models present limitations in these systems due to their complexity and unpredictability. To overcome this, the long short-term memory-based model is employed in this work. Our objective is to develop and evaluate a model to forecast the daily energy consumption of heating, ventilating, and air conditioning systems in buildings. For this purpose, we apply a comprehensive methodology that allows us to obtain a robust, generalizable, and reliable model by tuning different parameters. The results show that the proposed model achieves a significant improvement in the coefficient of variation of root mean square error of 9.5% compared to that proposed by international agencies. We conclude that these results provide an encouraging outlook for its implementation as an intelligent service for decision making, capable of overcoming the problems of other noise-sensitive models affected by data variations and disturbances without the need for expert knowledge in the domain.

Traffic forecasting at appropriate times is vital for a variety of urban traffic control applications. There are a plethora of factors that can severely affect the performance of such forecasting models, especially those unpredictable events that cannot be learned through previous time steps in the model. One of the important factors that can change the traffic flow pattern abruptly, is accident occurrence. This issue affects more when the severity of the accident is high which would lead to a complete anomaly behavior in the traffic flow pattern. In this paper, we detected the approximate time that the accidents happen through anomaly detection around the accident record and then pulled out the ones that change the traffic pattern suddenly. To predict the traffic flow after a severe accident, we propose a hybrid CNN-LSTM model that takes spatio-temporal matrices as discrete time sequences for each accident. Furthermore, we leverage distances between neighboring nodes and accident location, coupled with static features that are pertained to the accidents. Moreover, we evaluate our model on PeMs dataset which is enhanced with the static features of the accidents from Moosavi’s accident data set [1] [2]. Despite recent trials that are not able to forecast the results when an anomaly event happens, our results demonstrate that our proposed model can learn traffic patterns after the accidents and predict the traffic flow more accurately compared to current models.

The aim of the present study is to assess the combined use of geotechnical and electrical geophysical methods to determine water quality and rocks mechanics in an aquifer. The aquifer studied is located in the Tocumen sector of Panamá City, located to the southeast of city, where there is a need to study the possible use of this aquifer to provide drinking and/or irrigation water based on its quality. To this end, a 10 m well was perforated and sampled to characterize the host soil and rock trough granulometry, determine the Atterberg limits, measure the physicochemical parameters and perform a chemical analysis, including reactivity (pH), electrical conductivity (EC), organic matter content, cation exchange capacity, calcium carbonate, sulfates, chlorides, SiO2, Al2O3, Fe2O3, CaO, MgO, SO3, Na2O and K2O. In addition, a 2D electrical resistivity tomography profile was conducted in order to correlate the electric parameters with the physicochemical and chemical ones and extend them laterally to check the continuity of the characteristics measured. The results show a good correlation between geotechnical, geophysical and chemical parameters, thus highlighting the presence of discontinuities that must be overcome by infiltrated rainwater to reach the deepest levels, which are characterized by the presence of water. The water chemistry varies with depth, with sodium bicarbonated water being the predominant facies.

Mining affects the environment, particularly through the persistence of accumulation of tailings materials; this is aggravated under tropical climatic conditions, which favours the release of potentially toxic elements (PTEs) bioavailable to the local flora and fauna and supposing a risk to human health. The Remance gold mine (Panamá), exploited intermittently for more than 100 years, and has remained derelict for over 20 years. Within the area live farmers who carry out subsistence agriculture and livestock activities. The objective of this study has been to study the transference of PTEs in the local agricultural soil-plants system, with the goal of identifying their bioavailability to perform a human risk assessment. The results obtained of the Bioaccumulation coefficient in local plants show very weak to strong absorption of As ( 0.001-1.50), Hg ( 0.001-2.38), Sb (0.01-7.83), Cu (0.02-2.89), and Zn (0.06-5.32). In the case of Cu in grass (18.3 mg kg

In the nematode Caenorhabditis elegans, signals derived from bacteria in the diet, the animal's major nutrient source, can modulate both behavior and healthspan. Here we describe a dual role for trimethylamine (TMA), a human gut flora metabolite, which acts as a nutrient signal and a neurotoxin. TMA and its associated metabolites are produced by the human gut microbiome and have been suggested to serve as risk biomarkers for diabetes and cardiovascular diseases. We demonstrate that the tyramine receptor TYRA‐3, a conserved G protein‐coupled receptor (GPCR), is required to sense TMA and mediate its responses. TMA activates guanylyl cyclase DAF‐11 signaling through TYRA‐3 in amphid neurons (ASK) and ciliated neurons (BAG) to mediate food‐sensing behavior. Bacterial mutants deficient in TMA production enhance dauer formation, extend lifespan, and are less preferred as a food source. Increased levels of TMA lead to neural damage in models of Parkinson's disease and shorten lifespan. Our results reveal conserved signaling pathways modulated by TMA in C. elegans that are likely to be relevant for its effects in mammalian systems., TMA alters development, aging and neurodegeneration in C. elegans. Observed results suggests that the bacterial metabolite TMA alters development, aging and neurodegeneration in C. elegans through DAF‐11 and DAF‐16 pathways.

Pancreatic cancer is highly lethal and has a poor prognosis. The most common alteration during the formation of pancreatic tumors is the activation of KRAS (Kirsten rat sarcoma 2 viral oncogene homolog) oncogene. As a new therapeutic strategy, the C19 molecule ((2S)

The inadequate treatment of waste materials in mine tailings, the closure stage not carried out correctly, carries risks due to the residues of potentially toxic elements (PTEs) that it may contain. In Panama, environmental regulations are recent, and even so the regulations are not strictly enforced, leaving a problem for the adjacent communities. Such is the case of the Remance Gold Mine, in Veraguas, Panama; this area has been exploited intermittently for over a hundred years, by different companies; the last period of exploitation was between 1989 and 1999, through a cyanidation process to extract the precious metal. Currently three tailing dams with mining waste are exposed to climatic conditions such as rain and wind, in addition to this, within the mine area and its surroundings live peasants who carry out subsistence activities such as agriculture and livestock. The purpose of this study was to evaluate the remaining potentially toxic elements and the persistence of cyanide in the tailings, and whether the remaining quantity represents a risk to human health and the environment, according to current environmental regulations in Panama and America. For this purpose, samples have been taken from the tailings and adjacent areas that could be directly related to the cyanidation process, geochemical analysis such as pH, CEC, conductivity, organic matter, potentially toxic elements and total cyanide have been carried out. The most concerning aspects of the results obtained here have been the contents of Ba (between 55 and 610 mg kg-1), Zn (between 12 and 153 mg kg-1), Sb (between 0.6 and 25.5 mg kg-1) and Hg (between 0.1 and 3.2 mg kg-1), which, according to the criteria of land use quality standards in Panama and American countries, correspond to values above the permissible limits for agricultural and residential land use. Meanwhile the levels of As (between 17.3 and 5030 mg kg-1) and Cu (between 5.4 and 403 mg kg-1) are higher than the legally established values for industrial land use. Far more significant are the values for total cyanide (between 1.4 and 518 mg kg-1), revealing the persistence of this chemical over time. These cyanide values are far higher than those reported in the literature for solid tailings from an abandoned gold mine and the values for gold mine tailings in the Americas, thus representing a serious threat to the environment.

Organizations adopt software development methodologies and practices that are often not properly evaluated, triggering risk in their projects. Therefore, the objective of this systematic review is to examine detailed information on the impact and efficiency of Agile and/or Lean practices in organizations, and in this context to provide a basis for creating an ontological model and suggest parameters to consider before adopting software development practices. The synthesised results were obtained from four search engines: ScienceDirect, IEEE, Google Scholar and Springer. The information was categorized according to the research questions covering the foundations of the emergence of Agile and Lean practices, its contributions, impact and efficiency proven through parameters for creating a base ontology model concerning the faults and critical points in the proper use of Software development methodologies and practices as Agile/Lean in organizations. Additionally, this systematic review will allow for the creation like as future work of an ontology that identifies the organizational guidelines to consider before adopting a Software development methodology.

Indexación: Scopus Our aim is to cast light on socioeconomic residential segregation effects on life satisfaction (LS). In order to test our hypothesis, we use survey data from Chile (Casen) for the years 2011 and 2013. We use the Duncan Index to measure segregation based on income at the municipality level for 324 municipalities. LS is obtained from the CASEN survey, which considers a question about self-reported well-being. Segregation’s impact upon LS is not clear at first glance. On one hand, there is evidence telling that segregation’s consequences are negative due to the spatial concentration of poverty and all the woes related to it. On the other hand, segregation would have positive effects because people may feel stress, unhappiness, and alienation when comparing themselves to better-off households. Additionally, there is previous evidence regarding the fact that people prefer to neighbor people of a similar socioeconomic background. Hence, an empirical test is needed. In order to implement it, we should deal with two problems, first, the survey limited statistical significance at the municipal level, hence we use the small area estimation (SAE) methodology to improve the estimations’ statistic properties, and second, the double causality between segregation and LS; to deal with the latter, we include lagged LS as a regressor. Our findings indicate that socioeconomic segregation has a positive effect on LS. This result is robust to different econometric specifications. © Copyright © 2021 Montero, Vargas and Vásquez. https://www.frontiersin.org/articles/10.3389/fpsyg.2020.604194/full

Currently, smart buildings generate large amounts of data due to the many devices and equipment available. Hence, buildings implement building management systems (BMSs), which monitor, control, manage and analyze each of these components. However, current BMSs are incapable of managing a massive amount of data (big data) and therefore cannot extract knowledge or make intelligent decisions in quasi real time. In addition, there are serious limitations to integrating BMSs with other services since they generally use proprietary software. In this sense, service-oriented architecture (SOA) is an architectural style that allows one to build distributed systems and provide functionalities such as services to end users or other types of services. Therefore, an SOA has the great advantage of allowing the expansion of the functionalities of BMSs. In fact, there are several studies that address SOAs for building management. However, we have not found any description or systematic analysis in the literature that allows the development of a versatile and interoperable SOA focused on the energy efficiency of buildings and that can integrate massive data analysis features. For these reasons, this study seeks to fill this knowledge gap and, more specifically, to identify and analyze the various software requirements proposed in the literature and the characteristics of big data that allow for improving the energy efficiency of buildings. To this end, we performed an in-depth review of the literature according to the methodology proposed by Kitchenham. As a result of this review, we provide researchers with a specific vision of the requirements and characteristics to consider for software development aimed at the energy efficiency of unique or historic buildings.

A patient suffering from advanced chronic renal disease undergoes several dialysis sessions on different dates. Several clinical parameters are monitored during the different hours of any of these sessions. These parameters, together with the information provided by other parameters of analytical nature, can be very useful to determine the probability that a patient may suffer from hypotension during the session, which should be specially watched since it represents a proven factor of possible mortality. However, the analytical information is not always available to the healthcare personnel, or it is far in time, so the clinical parameters monitored during the session become key to the prevention of hypotension. This article presents an investigation to predict the appearance of hypotension during a dialysis session, using predictive models trained from a large dialysis database, which contains the clinical information of 98,015 sessions corresponding to 758 patients. The prediction model takes into account up to 22 clinical parameters measured five times during the session, as well as the gender and age of the patient. This model was trained by means of machine learning classifiers, providing a success in the prediction higher than 80%.

This paper investigates the structure and composition of the social network formed on the campus of the Faculty of Economics and Business of Diego Portales University, Chile, exposing a series of characteristics that are aligned with similar research in the field of networks. We use a model of social networks formation in order to understand socioeconomic and academic factors that predict the formation of friendship between two students. Specifically, we test empirically our model, using students' administrative information. Of special interest is the impact of the length of stay of the students in the university, with which we refer to the years completed in the degree course, in the probability of establishing friendship ties where being socioeconomically different is a condition. The mechanism behind a result like this is the sense of belongingness that being part of the same institution may induce amongst students. By means of counterfactual simulations we found evidence in favor that passing through the university increases the probability of forming friendship networks, which can mean a kind of social capital, thus reducing socioeconomic segregation from the Chilean school system. Given the importance of this finding, we believe that policies that increase the sense of belongingness such as cultural events, leaderships programs, and community should be implemented on university campuses.

We present a prototype whereby we enabled a humanoid robot to be used to assist mental health patients and their families. Our approach removes the need for Cloud-based automatic speech recognition systems to address healthcare privacy expectations. Furthermore, we describe how the robot could be used in a mental health facility by giving directions from patient selection to metrics for evaluation. Our overarching goal is to make the robot interaction as natural as possible to the point where the robot can develop artificial empathy for the human companion through the interpretation of vocals and facial expressions to infer emotions.

Nowadays, the application of methodologies that allow to guide the process of development of Software in the companies has become a fundamental aspect to achieve the suitable management of the processes in the projects. In view of the diversity of existing methodologies, there is a growing interest in defining strategies that allow the selection and application of the correct methodology, which adjusts to the characteristics of the work teams and the software projects. The aim of this work is to develop an ontology for the selection of the methodology that, according to its principles, is most appropriate and beneficial for the development of software projects. The domain of ontology is limited to the Agile and Lean approaches, without defining for Agile the specific method that it applies, but it involves any method that is governed by the agile values and principles established in the Agile manifesto. Onto-logy is applied in two organizations in the banking sector, allowing recommendations to be inferred for the use of Agile methodology in both, which will make it possible to reduce the delivery time of software products, improve communication between project participants, and facilitate the engineering of requirements. On the other hand, the ontology suggests co-regulating characteristic aspects of the Lean practices in order to minimize costs, optimize processes in the software projects and improve the organizational culture.

Para 1918, Europa vivía las consecuencias devastadoras de una pandemia, la Gripe Española. El virus se dispersó entre los soldados de la primera guerra mundial y a través de ellos a las poblaciones locales, generando decenas de millones de muertes en todo el mundo durante dos años. Un siglo después, el virus Sars-Covid19, es dispersado por los turistas que viajaban masivamente por todo el mundo desde China, suscitando la casi total parálisis del mundo. Se anhela el retorno al turismo normal, pero las condiciones para ello pueden haber cambiado de manera sustancial. Por eso, conviene revisar el proceso histórico del desarrollo turístico para identificar nuevos caminos a seguir, fijándonos en que las primeras definiciones y estudios sobre turismo surgieron en tiempos de pandemia y con ello, el surgimiento del turismo moderno. Paradójicamente, podría ser el mismo turismo el que contribuirá en la recuperación económica y social en tiempos de la pospandemia. El análisis que ofrece este libro aborda diversos spectos epistemológicos de las cuatro escuelas de pensamiento turístico (Económica, Del Ocio, Ambiental, institucional) y cómo éstas han incidido en la configuración de las propuestas de formación profesional para contribuir históricamente a la superación de la pobreza en las comunidades anfitrionas, protegiendo su patrimonio natural y cultural usufructuado por el turismo, al tiempo que se promueve la recreación dignificante del turista y se replantean las institucionalidades del turismo, entre las cuales se incluye las académicas. Los planes de estudios de pregrado y posgrado requieren de una renovación curricular que forme profesionales preparados para asumir al turismo desde el pensamiento complejo, para lo cual se debe educar desde modelos de gestión sistémicos, interdisciplinarios y participativos. Se incluye un estudio de caso con una propuesta para asumir procesos de renovación curricular para la formación en turismo.

In Panama City, the water supply sources for the population come mainly from surface waters, but in times of drought and extreme natural phenomena, the need to resort to underground sources is explored. This paper concerns an exploratory investigation with the aim of assessing the quality of groundwater in the southeastern part of the Province of Panama and to determine its possible use as drinking water during extreme natural phenomena. Monthly monitoring over a period of 15 months was carried out. The study included the sampling and analysis of groundwater by different physic-chemical, hydrochemical and biological parameters. The results showed that most of the parameters in the analysed groundwater conformed to the Panamanian drinking water norm and the water potability diagram, this is a convenient source of raw water for purification. As for other uses for this water source, it should be used with caution since it represents a medium risk of soil salinization for irrigation water; and it is a moderately hard water for industrial uses.

Lean Software Development (LSD) is a set of principles and practices that seek to increase the value of the organization by improving quality and soft-ware development processes, through value flow mapping and waste removal. Because of LSD’s contributions, many organizations are interested in implementing it; however, adopting the principles and practices of LSD without knowing the correct process leads to the premature abandonment of this methodology. Therefore, describing the implementation process through a formal and theoretical language, such as a basic ontology, allows you to have a greater perception of the elements to consider if you want to implement LSD. The objective of this research is to develop a base ontology for the implementation of Lean Software Development in an organization using a mature software development process.

The corporate organizational structure and culture should evolve in the development of software technology as the company grow; otherwise, it will present weaknesses that hinder the implementation of any software methodology. In this paper, the beneficial characteristics acquired by software products that have been developed under a DevOps conception have been identified, which has given DevOps a greater degree of relevance, promoting its study in greater detail, and assessing the growing interest of companies in having software as a quality that does not require additional costs for updating and maintenance. Additionally, the paper presents a systematic literature review, obtaining a conceptual schema of the relationship of DevOps with service-oriented software development, mitigating the lack of formalization of the concepts involved in each stage through an ontology. The result approximates a Model DevOps-based framework for SaaS development which can contribute as a guide for enterprises interested in the adoption of DevOps in their software production process for the first time.

PurposeCOVID-19 pandemic has multifaceted presentations with rising evidence of immune-mediated mechanisms underplay. We sought to explore the outcomes of severe COVID-19 patients treated with a multi-mechanism approach (MMA) in addition to standard-of-care (SC) versus patients who only received SC treatment.Materials and methodsData were collected retrospectively for patients admitted to the intensive care unit (ICU). This observational cohort study was performed at five institutions, 3 in the United States and 2 in Honduras. Patients were stratified for MMA vs. SC treatment during ICU stay. MMA treatment consists of widely available medications started immediately upon hospitalization. These interventions target immunomodulation, anticoagulation, viral suppression, and oxygenation. Primary outcomes included in-hospital mortality and length of stay (LOS) for the index hospitalization and were measured using logistic regression.ResultsOf 86 patients admitted, 65 (76%) who had severe COVID-19 were included in the study; 30 (46%) patients were in SC group, compared with 35 (54%) patients treated with MMA group. Twelve (40%) patients in the SC group died, compared with 5 (14%) in the MMA group (p-value = 0.01, Chi squared test). After adjustment for gender, age, treatment group, Q-SOFA score, the MMA group had a mean length of stay 8.15 days, when compared with SC group with 13.55 days. ICU length of stay was reduced by a mean of 5.4 days (adjusted for a mean age of 54 years, p-value 0.03) and up to 9 days (unadjusted for mean age), with no significant reduction in overall adjusted mortality rate, where the strongest predictor of mortality was the use of mechanical ventilation.ConclusionThe finding that MMA decreases the average ICU length of stay by 5.4 days and up to 9 days in older patients suggests that implementation of this treatment protocol could allow a healthcare system to manage 60% more COVID-19 patients with the same number of ICU beds.

Con la evolución del Internet, hay una gran cantidad de información presente en la web como lo son las opiniones de los usuarios o consumidores sobre diversos contextos ya sea para expresar su conformidad o inconformidad sobre un producto o servicio recibido, así como la opinión de un artículo comprado o sobre la gestión que realiza alguna persona. Debido a la gran cantidad de opiniones, comentarios y sugerencias de los usuarios, es muy importante explorar, analizar y organizar sus puntos de vista para tomar mejores decisiones. El análisis de sentimientos es una tarea de procesamiento de lenguaje natural y extracción de información que identifica las opiniones de los usuarios explicadas en forma de comentarios positivos, negativos o neutrales. Varias técnicas pueden ser utilizadas para este fin, por ejemplo el uso de diccionarios léxicos que ha sido muy utilizada y recientemente la utilización de la inteligencia artificial específicamente algoritmos supervisados. En este documento, se propone la utilización de técnicas de algoritmos supervisados para observar su utilización y ver el rendimiento de diferentes modelos de algoritmos supervisados para medir la efectividad en la clasificación de un conjunto de datos.