Hybrid model for early identification post-Covid-19 sequelae.

Artificial Intelligence techniques based on Machine Learning algorithms, Neural Networks and Naïve Bayes can optimise the diagnostic process of the SARS-CoV-2 or Covid-19. The most significant help of these techniques is analysing data recorded by health professionals when treating patients with this disease. Health professionals' more specific focus is due to the reduction in the number of observable signs and symptoms, ranging from an acute respiratory condition to severe pneumonia, showing an efficient form of attribute engineering. It is important to note that the clinical diagnosis can vary from asymptomatic to extremely harsh conditions. About 80% of patients with Covid-19 may be asymptomatic or have few symptoms. Approximately 20% of the detected cases require hospital care because they have difficulty breathing, of which about 5% may require ventilatory support in the Intensive Care Unit. Also, the present study proposes a hybrid approach model, structured in the composition of Artificial Intelligence techniques, using Machine Learning algorithms, associated with multicriteria methods of decision support based on the Verbal Decision Analysis methodology, aiming at the discovery of knowledge, as well as exploring the predictive power of specific data in this study, to optimise the diagnostic models of Covid-19. Thus, the model will provide greater accuracy to the diagnosis sought through clinical observation.

Hybrid model for early identification post-Covid-19 sequelae

Artificial Intelligence techniques based on Machine Learning algorithms, Neural Networks and Naïve Bayes can optimise the diagnostic process of the SARS-CoV-2 or Covid-19. The most significant help of these techniques is analysing data recorded by health professionals when treating patients with this disease. Health professionals' more specific focus is due to the reduction in the number of observable signs and symptoms, ranging from an acute respiratory condition to severe pneumonia, showing an efficient form of attribute engineering. It is important to note that the clinical diagnosis can vary from asymptomatic to extremely harsh conditions. About 80% of patients with Covid-19 may be asymptomatic or have few symptoms. Approximately 20% of the detected cases require hospital care because they have difficulty breathing, of which about 5% may require ventilatory support in the Intensive Care Unit. Also, the present study proposes a hybrid approach model, structured in the composition of Artificial Intelligence techniques, using Machine Learning algorithms, associated with multicriteria methods of decision support based on the Verbal Decision Analysis methodology, aiming at the discovery of knowledge, as well as exploring the predictive power of specific data in this study, to optimise the diagnostic models of Covid-19. Thus, the model will provide greater accuracy to the diagnosis sought through clinical observation.

An Intelligent Multicriteria Model for Diagnosing Dementia in People Infected with Human Immunodeficiency Virus

Hybrid models to detect dementia based on Machine Learning can provide accurate diagnoses in individuals with neurological disorders and cognitive complications caused by Human Immunodeficiency Virus (HIV) infection. This study proposes a hybrid approach, using Machine Learning algorithms associated with the multicriteria method of Verbal Decision Analysis (VDA). Dementia, which affects many HIV-infected individuals, refers to neurodevelopmental and mental disorders. Some manuals standardize the information used in the correct detection of neurological disorders with cognitive complications. Among the most common manuals used are the DSM-5 (Diagnostic and Statistical Manual of Mental Disorders, 5th edition) of the American Psychiatric Association and the International Classification of Diseases, 10th edition (ICD-10)—both published by World Health Organization (WHO). The model is designed to explore the predictive of specific data. Furthermore, a well-defined database data set improves and optimizes the diagnostic models sought in the research.

Psychological Distress in Men during the COVID-19 Pandemic in Brazil: The Role of the Sociodemographic Variables, Uncertainty, and Social Support.

OBJECTIVE: To analyze the relationships between sociodemographic variables, intolerance to uncertainty (INT), social support, and psychological distress (i.e., indicators of Common Mental Disorders (CMDs) and perceived stress (PS)) in Brazilian men during the COVID-19 pandemic. METHODS: A cross-sectional study with national coverage, of the web survey type, and conducted with 1006 Brazilian men during the period of social circulation restriction imposed by the health authorities in Brazil for suppression of the coronavirus and control of the pandemic. Structural equation modeling analysis was performed. RESULTS: Statistically significant direct effects of race/skin color (λ = 0.268; p-value < 0.001), socioeconomic status (SES) (λ = 0.306; p-value < 0.001), household composition (λ = 0.281; p-value < 0.001), PS (λ = 0.513; p-value < 0.001), and INT (λ = 0.421; p-value < 0.001) were evidenced in the occurrence of CMDs. Black-skinned men with higher SES, living alone, and with higher PS and INT levels presented higher prevalence values of CMDs. CONCLUSIONS: High levels of PS and INT were the factors that presented the strongest associations with the occurrence of CMDs among the men. It is necessary to implement actions to reduce the stress-generating sources as well as to promote an increase in resilience and the development of intrinsic reinforcements to deal with uncertain threats.

Application of Data Mining Algorithms for Dementia in People with HIV/AIDS.

Dementia interferes with the individual's motor, behavioural, and intellectual functions, causing him to be unable to perform instrumental activities of daily living. This study is aimed at identifying the best performing algorithm and the most relevant characteristics to categorise individuals with HIV/AIDS at high risk of dementia from the application of data mining. Principal component analysis (PCA) algorithm was used and tested comparatively between the following machine learning algorithms: logistic regression, decision tree, neural network, KNN, and random forest. The database used for this study was built from the data collection of 270 individuals infected with HIV/AIDS and followed up at the outpatient clinic of a reference hospital for infectious and parasitic diseases in the State of Ceará, Brazil, from January to April 2019. Also, the performance of the algorithms was analysed for the 104 characteristics available in the database; then, with the reduction of dimensionality, there was an improvement in the quality of the machine learning algorithms and identified that during the tests, even losing about 30% of the variation. Besides, when considering only 23 characteristics, the precision of the algorithms was 86% in random forest, 56% logistic regression, 68% decision tree, 60% KNN, and 59% neural network. The random forest algorithm proved to be more effective than the others, obtaining 84% precision and 86% accuracy.

Optimal control of the COVID-19 pandemic: controlled sanitary deconfinement in Portugal.

The COVID-19 pandemic has forced policy makers to decree urgent confinements to stop a rapid and massive contagion. However, after that stage, societies are being forced to find an equilibrium between the need to reduce contagion rates and the need to reopen their economies. The experience hitherto lived has provided data on the evolution of the pandemic, in particular the population dynamics as a result of the public health measures enacted. This allows the formulation of forecasting mathematical models to anticipate the consequences of political decisions. Here we propose a model to do so and apply it to the case of Portugal. With a mathematical deterministic model, described by a system of ordinary differential equations, we fit the real evolution of COVID-19 in this country. After identification of the population readiness to follow social restrictions, by analyzing the social media, we incorporate this effect in a version of the model that allow us to check different scenarios. This is realized by considering a Monte Carlo discrete version of the previous model coupled via a complex network. Then, we apply optimal control theory to maximize the number of people returning to "normal life" and minimizing the number of active infected individuals with minimal economical costs while warranting a low level of hospitalizations. This work allows testing various scenarios of pandemic management (closure of sectors of the economy, partial/total compliance with protection measures by citizens, number of beds in intensive care units, etc.), ensuring the responsiveness of the health system, thus being a public health decision support tool.