A Judicialização da Saúde na era do Big Data / The judicialization of Health in the era of Big Data / La Judicialización de la Salud en la era del Big Data

No Brasil, a judicialização da saúde, que é o uso de ações judiciais para garantir acesso a medicamentos e serviços de saúde, atende a interesses diversos e torna mais complexa a gestão das políticas públicas de saúde. A produção de estatísticas sobre essas ações é desafiadora, pois existem grandes divergências entre fontes de dados. Em Minas Gerais há um número significativo de ações, com grandes desafios na classificação e na padronização das informações. A pesquisa realizada mostrou que, com técnicas de mineração de dados e padronização da definição de processos de saúde, é possível avançar nessa direção. Ferramentas como o JUDJe objetivam facilitar a compreensão da judicialização da saúde, utilizando tecnologia de ponta e respeitando as regras de privacidade e segurança. Conclui-se pela necessidade de incorporar amplamente essas ferramentas e padronizar os assuntos, para beneficiar a gestão do setor de saúde em todo o Brasil.

The judicialization of health in Brazil, through legal actions to guarantee access to medicines and health services, complicates health policies and serves different interests. Accurately accounting for these actions is challenging, highlighting divergences between data sources. Analysis of data from Minas Gerais reveals significant numbers, but highlights challenges in classifying and standardizing information. Research showed that, with data mining techniques and standardization of the definition of health processes, it is possible to move in this direction. Tools like JUDJe promise to improve understanding of the judicialization of health, facing technological, privacy and security challenges. It is concluded that there is a need to widely incorporate these tools and standardize matters, to benefit the management of the health sector throughout Brazil.

La judicialización de la salud en Brasil, a través de acciones legales para garantizar el acceso a medicamentos y servicios de salud, complica las políticas de salud y atiende intereses diferentes. Contabilizar con precisión estas acciones es un desafío, lo que pone de relieve las divergencias entre las fuentes de datos. El análisis de datos de Minas Gerais revela cifras significativas, pero destaca los desafíos en la clasificación y estandarización de la información. Investigaciones muestran que, con técnicas de minería de datos y estandarización de la definición de procesos de salud, es posible avanzar en esa dirección. Herramientas como JUDJe prometen mejorar la comprensión de la judicialización de la salud, enfrentando desafíos tecnológicos, de privacidad y de seguridad. Se concluye que es necesario incorporar ampliamente estas herramientas y estandarizar las materias, para beneficiar la gestión del sector salud en todo Brasil.

Caracterización Multimodal de pacientes pediátricos con enfermedades neuromusculares del Suroccidente Colombiano

Introducción: La Distrofia Muscular de Duchenne (DMD) y la Atrofia Muscular Espinal (AME) son enfermedades neuromusculares genéticas poco comunes pero graves en la población pediátrica con alta carga de morbilidad y mortalidad. A pesar de avances en su comprensión y búsqueda de opciones terapéuticas dirigidas, persisten vacíos en la detección oportuna, caracterización, seguimiento de pacientes, búsqueda activa de portadores y en algunos países de Latinoamérica sin tamización neonatal. Objetivo: Caracterizar clínica, paraclínica, imagenológica y molecularmente pacientes con diagnóstico presuntivo y confirmado de distrofia muscular de Duchenne (DMD) y Atrofia Muscular Espinal (AME) atendidos en un centro pediátrico de referencia y excelencia del Suroccidente Colombiano. Materiales y Métodos: Estudio observacional de corte transversal en pacientes menores de 18 años con diagnósticos CIE-10 relacionados con DMD y AME. Los datos se exportaron a una matriz de Excel en Office 365 versión 2403, y luego a IBM SPSS versión 29 para realizar un análisis univariado, se emplearon medidas de tendencia central y dispersión para variables numéricas, considerando su distribución, y frecuencias absolutas y porcentajes para variables cualitativas. Resultados: Tras revisar 954 historias clínicas pertenecientes a un centro de atención pediátrica en el Suroccidente Colombiano entre los años 2015 - 2021, se identificaron 422 casos relacionados a Distrofia Muscular de Duchenne (DMD) y Atrofia Muscular Espinal (AME); excluyendo duplicados y registros no relacionados, de estos, se seleccionaron aleatoriamente 99 casos para un análisis exhaustivo utilizando OpenEpi versión 3.01, distribuidos en dos grupos: AME (n=23) y DMD (n=76). Los pacientes confirmados con Distrofia Muscular de Duchenne (DMD) mostraron un inicio de síntomas a los 54,5 ± 29,0 meses y un diagnóstico a los 98,8 ± 34,9 meses, siendo más común en varones con hipotonía y niveles elevados de creatin quinasa (CK), el 54,5% presentaba trastorno cognitivo y el 88.2% tenía antecedentes familiares, en la Atrofia Muscular Espinal (AME), el inicio de síntomas fue a los 28,9 ± 37,7 meses y el diagnóstico a los 37,9 ± 38,2 meses, siendo predominante en mujeres con arreflexia y fasciculaciones, no hubo registros de la función cognitiva en los pacientes confirmados, y el 21,7% tenía antecedentes familiares de AME, además de ligeras elevaciones de CK. En el grupo AME, 9 casos se confirmaron molecularmente y 3 se respaldaron con registros médicos; en contraste, en el grupo de DMD, 22 casos tuvieron confirmación molecular, pero 9 contaban con anotaciones en los registros médicos, aunque estos informes eran incompletos. Conclusiones: La sospecha y diagnóstico temprano de estas enfermedades neurodegenerativas progresivas que se caracterizan por altas tasas de morbilidad y mortalidad es fundamental para impactar en el abordaje holístico que deben recibir los pacientes. Dado al continuo avance en métodos diagnósticos y opciones terapéuticas innovadoras y dirigidas ( medicina de la hiperpersonalización ), se hace necesario crear registros y "big data" médicos- clínicos completos, que cuenten con todas las herramientas actuales disponibles (opciones diagnósticas multimodales) que faciliten el re-contacto de pacientes, seguimiento y poderles ofrecer una atención personalizada, de precisión, que mejore la calidad de vida de ellos, sus familias, contribuyendo en la generación de políticas públicas integradas y dirigidas. (provisto por Infomedic International)

Introduction: Duchenne Muscular Dystrophy (DMD) and Spinal Muscular Atrophy (SMA) are rare but severe genetic neuromuscular diseases in the pediatric population with high burden of morbidity and mortality. Despite advances in their understanding and search for targeted therapeutic options, there are still gaps in timely detection, characterization, patient follow-up, active search for carriers and in some Latin American countries no neonatal screening. Objective: To characterize clinically, paraclinically, imaging and molecularly patients with presumptive and confirmed diagnosis of Duchenne muscular dystrophy (DMD) and Spinal Muscular Atrophy (SMA) attended in a pediatric center of reference and excellence in Southwestern Colombia. Materials and Methods: Observational cross-sectional study in patients under 18 years of age with ICD-10 diagnoses related to DMD and SMA. Data were exported to an Excel matrix in Office 365 version 2403, and then to IBM SPSS version 29 to perform a univariate analysis, measures of central tendency and dispersion were used for numerical variables, considering their distribution, and absolute frequencies and percentages for qualitative variables. Results: After reviewing 954 medical records belonging to a pediatric care center in Southwestern Colombia between 2015 - 2021, 422 cases related to Duchenne Muscular Dystrophy (DMD) and Spinal Muscular Atrophy (SMA) were identified; excluding duplicates and unrelated records, from these, 99 cases were randomly selected for a comprehensive analysis using OpenEpi version 3.01, distributed in two groups: SMA (n=23) and DMD (n=76). Patients confirmed with Duchenne Muscular Dystrophy (DMD) showed symptom onset at 54.5 ± 29.0 months and diagnosis at 98.8 ± 34.9 months, being more common in males with hypotonia and elevated creatin kinase (CK) levels, 54.5% had cognitive impairment and 88. 2% had family history, in Spinal Muscular Atrophy (SMA), the onset of symptoms was at 28.9 ± 37.7 months and diagnosis at 37.9 ± 38.2 months, being predominant in females with areflexia and fasciculations, there were no records of cognitive function in confirmed patients, and 21.7% had family history of SMA, in addition to slight elevations of CK. In the SMA group, 9 cases were molecularly confirmed and 3 were supported by medical records; in contrast, in the DMD group, 22 cases had molecular confirmation, but 9 had annotations in medical records, although these reports were incomplete. Conclusions: Early suspicion and diagnosis of these progressive neurodegenerative diseases characterized by high morbidity and mortality rates is critical to impact the holistic approach patients should receive. Given the continuous advance in diagnostic methods and innovative and targeted therapeutic options (hyperpersonalization medicine), it is necessary to create complete medical-clinical registries and big data, which have all the current tools available (multimodal diagnostic options) to facilitate patient re-contact, follow-up and to be able to offer personalized, precision care that improves the quality of life of patients and their families, contributing to the generation of integrated and targeted public policies. (provided by Infomedic International)

Custos sociais da DPOC: o impacto sobre os anos de vida saudável e a perda de produtividade no Brasil entre os anos de 2017 e 2022 / Social costs of COPD: The impact on healthy life years and productivity loss in Brazil from 2017 to 2022

Objetivo: Estimar as perdas de produtividade causadas pela doença pulmonar obstrutiva crônica (DPOC) na população brasileira. Métodos: O estudo utilizou dados obtidos do Datasus, IBGE, indicadores previdenciários, óbitos e aposentadorias precoces por DPOC no Brasil de 2017 a 2022. Para estimar o impacto da DPOC, foram utilizados: anos de vida saudável perdidos (DALYs) e anos de vida ajustados por produtividade (PALYs), assim como as métricas de perda de produtividade salarial (PPS) e perda de produtividade nacionalizada (PPN), que avalia a perda em função do PIB. Resultados: Mais de 196 milhões de dias de trabalho foram perdidos devido à DPOC. As principais fontes são: óbitos precoces (95.264.088), afastamentos permanentes (67.314.232) e aposentadoria precoce (30.304.490). Diárias hospitalares (3.221.591) têm uma contribuição minoritária. O valor total de DALYs observado no período do estudo foi de 2.819.332,63 anos de vida saudável perdidos causados pela DPOC; um total de 14.997.166 PALYs foi perdido por conta da DPOC ou um valor anual equivalente de R$ 230,7 bilhões. Considerando a PPS, estimamos que a DPOC acarretou perdas de produtividade associadas à reposição da mão de obra de R$ 1,38 bilhão anual e, em relação à PPN, de R$ 8,28 bilhões por ano. Conclusões: Afastamentos de pacientes com DPOC podem acarretar maiores dispêndios com pagamentos de benefícios previdenciários. Este estudo atualiza e amplia correlações entre dados socioepidemiológicos, custos de saúde e previdenciários da DPOC no Brasil. Considerando todas as perdas, a DPOC pode causar perdas de R$ 240 bilhões por ano.

Objective: To estimate productivity losses due to workdays lost caused by chronic obstructive pulmonary disease (COPD) in the Brazilian population. Methods: The study used data from DATASUS, IBGE, social security indicators, deaths, and early retirements due to COPD in Brazil from 2017 to 2022. To estimate the impact of COPD, the following were used: Disability-Adjusted Life Years (DALYs) and Productivity-Adjusted Life Years (PALYs), as well as metrics for wage productivity loss (PPS) and nationalized productivity loss (PPN), which evaluates the loss in relation to GDP. Results: More than 196 million workdays were lost due to COPD. The main sources are premature deaths (95,264,088), permanent absences (67,314,232), and early retirement (30,304,490). Hospitalization days (3,221,591) had a minor contribution. The total DALYs observed during the study period was 2,819,332.63 years of healthy life lost due to COPD; a total of 14,997,166 PALYs were lost due to COPD, equivalent to an annual value of R$ 230.7 billion. Considering PPS, we estimate that COPD resulted in productivity losses associated with workforce replacement of R$ 1.38 billion annually; and in relation to PPN, R$ 8.28 billion per year. Conclusions: Absences in COPD patients can lead to higher expenditures on social security benefit payments. This is the first study to correlate socioepidemiological data, health, and social security costs of COPD in Brazil. Considering all losses, COPD can cause losses of R$ 240 billion per year.

An Information Model on Pain Management: Cultural Validation and Refinement.

This study aimed to validate and refine an information model on pain management in a Brazilian hospital, considering the institutional culture, using an expert consensus approach. The first stage took place through a computerized questionnaire and Content Validity Index calculation. Pain management attributes were considered validated with 75% consensus among 19 experts. The second stage validated and refined the information model by three experts via an online meeting. Results showed that out of 11 evaluated attributes, five were validated. In the second stage, the inclusion of new attributes was suggested to address institutional culture. The final information model resulted from 23 sets of revised attributes: 12 validated, seven suggested and four not validated. The resulting Brazilian model has the potential to support the implementation of interventions and propose improvements to the institution's electronic system, which can be reused in other institutions.

The relationship between online searches and suicide.

AIMS: In this study, we examined the relationship between 131 suicide related Google search terms, grouped into nine categories, and the number of suicide cases per month in Ecuador from January 2011 to December 2021. METHODS: First, we applied time-series analysis to eliminate autocorrelation and seasonal patterns to prevent spurious correlations. Second, we used Pearson's correlation to assess the relationship between Google search terms and suicide rates. Third, cross-correlation analysis was used to explore the potential delayed effects between these variables. Fourth, we extended the correlation and cross-correlation analyses by three demographic characteristics - gender, age, and region. RESULTS: Significant correlations were found in all categories between Google search trends and suicide rates in Ecuador, with predominantly positive and moderate correlations. The terms 'stress' (.548), 'prevention' (.438), and 'disorders' (.435) showed the strongest associations. While global trends indicated moderate correlations, sensitivity analysis revealed higher coefficients in men, young adults, and the Highlands region. Specific patterns emerged in subgroups, such as 'digital violence' showing significant correlations in certain demographics, and 'trauma' presenting a unique temporal pattern in women. In general, cross correlation analysis showed an average negative correlation of -.191 at lag 3. CONCLUSION: Google search data do not provide further information about users, such as demographics or mental health records. Hence, our results are simply correlations and should not be interpreted as causal effects. Our findings highlight a need for tailored suicide prevention strategies that recognize the complex dynamics of suicide risk across demographics and time periods.

Current Trends in Artificial Intelligence and Bovine Mastitis Research: A Bibliometric Review Approach.

Mastitis, an important disease in dairy cows, causes significant losses in herd profitability. Accurate diagnosis is crucial for adequate control. Studies using artificial intelligence (AI) models to classify, identify, predict, and diagnose mastitis show promise in improving mastitis control. This bibliometric review aimed to evaluate AI and bovine mastitis terms in the most relevant Scopus-indexed papers from 2011 to 2021. Sixty-two documents were analyzed, revealing key terms, prominent researchers, relevant publications, main themes, and keyword clusters. "Mastitis" and "machine learning" were the most cited terms, with an increasing trend from 2018 to 2021. Other terms, such as "sensors" and "mastitis detection", also emerged. The United States was the most cited country and presented the largest collaboration network. Publications on mastitis and AI models notably increased from 2016 to 2021, indicating growing interest. However, few studies utilized AI for bovine mastitis detection, primarily employing artificial neural network models. This suggests a clear potential for further research in this area.

Integrating perspectives in developing mycorrhizal trait databases: a call for inclusive and collaborative continental efforts.

Global assessments of mycorrhizal symbiosis present large sampling gaps in rich biodiversity regions. Filling these gaps is necessary to build large-scale, unbiased mycorrhizal databases to obtain reliable analyses and prevent misleading generalizations. Underrepresented regions in mycorrhizal research are mainly in Africa, Asia, and South America. Despite the high biodiversity and endemism in these regions, many groups of organisms remain understudied, especially mycorrhizal fungi. In this Viewpoint, we emphasize the importance of inclusive and collaborative continental efforts in integrating perspectives for comprehensive trait database development and propose a conceptual framework that can help build large mycorrhizal databases in underrepresented regions. Based on the four Vs of big data (volume, variety, veracity, and velocity), we identify the main challenges of constructing a large mycorrhizal dataset and propose solutions for each challenge. We share our collaborative methodology, which involves employing open calls and working groups to engage all mycorrhizal researchers in the region to build a South American Mycorrhizal Database. By fostering interdisciplinary collaborations and embracing a continental-scale approach, we can create robust mycorrhizal trait databases that provide valuable insights into the evolution, ecology, and functioning of mycorrhizal associations, reducing the geographical biases that are so common in large-scale ecological studies.

Effect of the COVID-19 pandemic on mental health visits in primary care: an interrupted time series analysis from nine INTRePID countries.

Background: The COVID-19 pandemic impacted mental health disorders, affecting both individuals with pre-existing conditions and those with no prior history. However, there is limited evidence regarding the pandemic's impact on mental health visits to primary care physicians. The International Consortium of Primary Care Big Data Researchers (INTRePID) explored primary care visit trends related to mental health conditions in Argentina, Australia, Canada, China, Norway, Peru, Singapore, Sweden, and the USA. Methods: We conducted an interrupted time series analysis in nine countries to examine changes in rates of monthly mental health visits to primary care settings from January 1st, 2018, to December 31st, 2021. Sub-group analysis considered service type (in-person/virtual) and six categories of mental health conditions (anxiety/depression, bipolar/schizophrenia/other psychotic disorders, sleep disorders, dementia, ADHD/eating disorders, and substance use disorder). Findings: Mental health visit rates increased after the onset of the pandemic in most countries. In Argentina, Canada, China, Norway, Peru, and Singapore, this increase was immediate ranged from an incidence rate ratio of 1·118 [95% CI 1.053-1.187] to 2.240 [95% CI 2.057-2.439] when comparing the first month of pandemic with the pre-pandemic trend. Increases in the following months varied across countries. Anxiety/depression was the leading reason for mental health visits in most countries. Virtual visits were reported in Australia, Canada, Norway, Peru, Sweden, and the USA, accounting for up to 40% of the total mental health visits. Interpretation: Findings suggest an overall increase in mental health visits, driven largely by anxiety/depression. During the COVID-19 pandemic, many of the studied countries adopted virtual care in particular for mental health visits. Primary care plays a crucial role in addressing mental ill-health in times of crisis. Funding: Canadian Institutes of Health Research grant #173094 and the Rathlyn Foundation Primary Care EMR Research and Discovery Fund.

Identifying biologically implausible values in big longitudinal data: an example applied to child growth data from the Brazilian food and nutrition surveillance system.

BACKGROUND: Several strategies for identifying biologically implausible values in longitudinal anthropometric data have recently been proposed, but the suitability of these strategies for large population datasets needs to be better understood. This study evaluated the impact of removing population outliers and the additional value of identifying and removing longitudinal outliers on the trajectories of length/height and weight and on the prevalence of child growth indicators in a large longitudinal dataset of child growth data. METHODS: Length/height and weight measurements of children aged 0 to 59 months from the Brazilian Food and Nutrition Surveillance System were analyzed. Population outliers were identified using z-scores from the World Health Organization (WHO) growth charts. After identifying and removing population outliers, residuals from linear mixed-effects models were used to flag longitudinal outliers. The following cutoffs for residuals were tested to flag those: -3/+3, -4/+4, -5/+5, -6/+6. The selected child growth indicators included length/height-for-age z-scores and weight-for-age z-scores, classified according to the WHO charts. RESULTS: The dataset included 50,154,738 records from 10,775,496 children. Boys and girls had 5.74% and 5.31% of length/height and 5.19% and 4.74% of weight values flagged as population outliers, respectively. After removing those, the percentage of longitudinal outliers varied from 0.02% (<-6/>+6) to 1.47% (<-3/>+3) for length/height and from 0.07 to 1.44% for weight in boys. In girls, the percentage of longitudinal outliers varied from 0.01 to 1.50% for length/height and from 0.08 to 1.45% for weight. The initial removal of population outliers played the most substantial role in the growth trajectories as it was the first step in the cleaning process, while the additional removal of longitudinal outliers had lower influence on those, regardless of the cutoff adopted. The prevalence of the selected indicators were also affected by both population and longitudinal (to a lesser extent) outliers. CONCLUSIONS: Although both population and longitudinal outliers can detect biologically implausible values in child growth data, removing population outliers seemed more relevant in this large administrative dataset, especially in calculating summary statistics. However, both types of outliers need to be identified and removed for the proper evaluation of trajectories.

Editor's Choice - Analysis of Lower Limb Amputations in Brazil's Public Health System over 13 Years.

OBJECTIVE: Understanding the causes of amputation is crucial for defining health policies that seek to avoid such an outcome, but only a few studies have investigated the epidemiology of patients submitted to amputations in developing countries. The objective of this study was to analyse all lower limb amputations performed in the public health system in Brazil over a 13 year period, evaluating trends in the number of cases, patient demographics, associated aetiologies, hospital length of stay, and in hospital mortality rate. METHODS: This was a retrospective, population based analysis of all lower limb amputations performed in the Brazilian public health system between 1 January 2008 and 31 December 2020. Using a public database, all types of amputations were selected, defining the number of procedures, their main aetiologies, anatomical level of limb loss, demographic data, regional distribution, and other variables of interest. RESULTS: A total of 633 455 amputations were performed between 2008 and 2020, mostly (55.6%) minor amputations, predominantly in males (67%). There was an upward trend in the number of amputations, determined mainly by the increase in major amputations (50.4% increase in the period). Elderly individuals have the highest rates of amputation. Diabetes mellitus (DM) is becoming the main primary diagnosis associated with amputations over the years. The highest in hospital mortality rate occurred after major amputations and was associated with peripheral arterial disease (PAD). CONCLUSION: Amputation rates in Brazil show an upward trend. DM is becoming the most frequent associated primary diagnosis, although PAD is the diagnosis most associated with major amputations and in hospital death.

A data-driven approach to decode metabolic dysfunction-associated steatotic liver disease.

Metabolic dysfunction-associated steatotic liver disease (MASLD), defined by the presence of liver steatosis together with at least one out of five cardiometabolic factors, is the most common cause of chronic liver disease worldwide, affecting around one in three people. Yet the clinical presentation of MASLD and the risk of progression to cirrhosis and adverse clinical outcomes is highly variable. It, therefore, represents both a global public health threat and a precision medicine challenge. Artificial intelligence (AI) is being investigated in MASLD to develop reproducible, quantitative, and automated methods to enhance patient stratification and to discover new biomarkers and therapeutic targets in MASLD. This review details the different applications of AI and machine learning algorithms in MASLD, particularly in analyzing electronic health record, digital pathology, and imaging data. Additionally, it also describes how specific MASLD consortia are leveraging multimodal data sources to spark research breakthroughs in the field. Using a new national-level 'data commons' (SteatoSITE) as an exemplar, the opportunities, as well as the technical challenges of large-scale databases in MASLD research, are highlighted.

Climate risk zoning for wheat crops in the southeastern region of Brazil.

BACKGROUND: Wheat (Triticum aestivum L.) is the second most consumed food in the world. One way to meet this demand is the expansion of wheat cultivation to the Brazilian Cerrado in the southeastern region. However, one of the major limitations is that there are few studies related to wheat climate risk zoning. Thus, this study aimed to determine the agroclimatic zoning of wheat by estimating the water needs satisfaction index (ISNA) in the southeastern region of Brazil. For this purpose, a 60-year historical series of meteorological data was used to calculate the potential evapotranspiration, crop evapotranspiration, and climatological water balance values. To define the agroclimatic zones of wheat and sowing date, the ISNA method was used. The data were analyzed using descriptive statistics to determine the variations. To obtain the agroclimatic zoning of wheat, the geostatistical method of kriging interpolation was used. RESULTS: The regions with the highest rainfall are the south of Minas Gerais and the coast of São Paulo. The sowing period directly impacts the development of the crop, the available water capacity and the ISNA values indicated the spring and summer had better cultivation conditions, and the best window for wheat cultivation is concentrated in the fall due to the limitation of biotic factors. CONCLUSION: In terms of altitude (>700 m), Minas Gerais has 39.4% of the area suitable for wheat cultivation. Thus, climatic variations within and between the states of the southeastern region should be considered for the positioning of wheat cultivars in these regions to obtain the maximum yield. © 2023 Society of Chemical Industry.

Is it possible to estimate the number of patients with COVID-19 admitted to intensive care units and general wards using clinical and telemedicine data?

ABSTRACT Objective: To develop and validate predictive models to estimate the number of COVID-19 patients hospitalized in the intensive care units and general wards of a private not-for-profit hospital in São Paulo, Brazil. Methods: Two main models were developed. The first model calculated hospital occupation as the difference between predicted COVID-19 patient admissions, transfers between departments, and discharges, estimating admissions based on their weekly moving averages, segmented by general wards and intensive care units. Patient discharge predictions were based on a length of stay predictive model, assessing the clinical characteristics of patients hospitalized with COVID-19, including age group and usage of mechanical ventilation devices. The second model estimated hospital occupation based on the correlation with the number of telemedicine visits by patients diagnosed with COVID-19, utilizing correlational analysis to define the lag that maximized the correlation between the studied series. Both models were monitored for 365 days, from May 20th, 2021, to May 20th, 2022. Results: The first model predicted the number of hospitalized patients by department within an interval of up to 14 days. The second model estimated the total number of hospitalized patients for the following 8 days, considering calls attended by Hospital Israelita Albert Einstein's telemedicine department. Considering the average daily predicted values for the intensive care unit and general ward across a forecast horizon of 8 days, as limited by the second model, the first and second models obtained R² values of 0.900 and 0.996, respectively and mean absolute errors of 8.885 and 2.524 beds, respectively. The performances of both models were monitored using the mean error, mean absolute error, and root mean squared error as a function of the forecast horizon in days. Conclusion: The model based on telemedicine use was the most accurate in the current analysis and was used to estimate COVID-19 hospital occupancy 8 days in advance, validating predictions of this nature in similar clinical contexts. The results encourage the expansion of this method to other pathologies, aiming to guarantee the standards of hospital care and conscious consumption of resources.

A multi-omics approach to understand the influence of polyphenols in ovarian cancer for precision nutrition: a mini-review.

The impact of polyphenols in ovarian cancer is widely studied observing gene expression, epigenetic alterations, and molecular mechanisms based on new 'omics' technologies. Therefore, the combination of omics technologies with the use of phenolic compounds may represent a promising approach to precision nutrition in cancer. This article provides an updated review involving the current applications of high-throughput technologies in ovarian cancer, the role of dietary polyphenols and their mechanistic effects in ovarian cancer, and the current status and challenges of precision nutrition and their relationship with big data. High-throughput technologies in different omics science can provide relevant information from different facets for identifying biomarkers for diagnosis, prognosis, and selection of specific therapies for personalized treatment. Furthermore, the field of omics sciences can provide a better understanding of the role of polyphenols and their function as signaling molecules in the prevention and treatment of ovarian cancer. Although we observed an increase in the number of investigations, there are several approaches to data acquisition, analysis, and integration that still need to be improved, and the standardization of these practices still needs to be implemented in clinical trials.

AgDataBox-IoT - application development for agrometeorological stations in smart.

Currently, Brazil is one of the world's largest grain producers and exporters. Agriculture has already entered its 4.0 version (2017), also known as digital agriculture, when the industry has entered the 4.0 era (2011). This new paradigm uses Internet of Things (IoT) techniques, sensors installed in the field, network of interconnected sensors in the plot, drones for crop monitoring, multispectral cameras, storage and processing of data in Cloud Computing, and Big Data techniques to process the large volumes of generated data. One of the practical options for implementing precision agriculture is the segmentation of the plot into management zones, aiming at maximizing profits according to the productive potential of each zone, being economically viable even for small producers. Considering that climate factors directly influence yield, this study describes the development of a sensor network for climate monitoring of management zones (microclimates), allowing the identification of climate factors that influence yield at each of its stages.•Application of the internet of things to assist in decision making in the agricultural production system.•AgDataBox (ADB-IoT) web platform has an Application Programming Interface (API).•An agrometeorological station capable of monitoring all meteorological parameters was developed (Kate 3.0).

Exploring Large Digital Bodies for the Study of Human Behavior.

Internet access has become a fundamental component of contemporary society, with major impacts in many areas that offer opportunities for new research insights. The search and deposition of information in digital media form large sets of data known as digital corpora, which can be used to generate structured data, representing repositories of knowledge and evidence of human culture. This information offers opportunities for scientific investigations that contribute to the understanding of human behavior on a large scale, reaching human populations/individuals that would normally be difficult to access. These tools can help access social and cultural varieties worldwide. In this article, we briefly review the potential of these corpora in the study of human behavior. Therefore, we propose Culturomics of Human Behavior as an approach to understand, explain, and predict human behavior using digital corpora.

Factors for the implementation of the circular economy in Big Data environments in service companies in post pandemic times of COVID-19: The case of Colombia.

In emerging economies, Big Data (BD) analytics has become increasingly popular, particularly regarding the opportunities and expected benefits. Such analyzes have identified that the production and consumption of goods and services, while unavoidable, have proven to be unsustainable and inefficient. For this reason, the concept of the circular economy (CE) has emerged strongly as a sustainable approach that contributes to the eco-efficient use of resources. However, to develop a circular economy in DB environments, it is necessary to understand what factors influence the intention to accept its implementation. The main objective of this research was to assess the influence of attitudes, subjective norms, and perceived behavioral norms on the intention to adopt CE in BD-mediated environments. The methodology is quantitative, cross-sectional with a descriptive correlational approach, based on the theory of planned behavior and a Partial Least Squares Structural Equation Model (PLS-SEM). A total of 413 Colombian service SMEs participated in the study. The results show that managers' attitudes, subjective norms, and perceived norms of behavior positively influence the intentions of organizations to implement CB best practices. Furthermore, most organizations have positive intentions toward CE and that these intentions positively influence the adoption of DB; however, the lack of government support and cultural barriers are perceived as the main limitation for its adoption. The research leads to the conclusion that BD helps business and government develop strategies to move toward CE, and that there is a clear positive will and intent toward a more restorative and sustainable corporate strategy.

Architecture of a Data Portal for Publishing and Delivering Open Data for Atmospheric Measurement.

Atmospheric data are collected by researchers every day. Campaigns such as GOAmazon 2014/2015 and the Amazon Tall Tower Observatory collect essential data on aerosols, gases, cloud properties, and meteorological parameters in the Brazilian Amazon basin. These data products provide insights and essential information for analyzing and predicting natural processes. However, in Brazil, it is estimated that more than 80% of the scientific data collected are not published due to the lack of web portals that collect and store these data. This makes it difficult, or even impossible, to access and integrate the data, which can result in the loss of significant amounts of information and significantly affect the understanding of the overall data. To address this problem, we propose a data portal architecture and open data deployment that enable Big Data processing, human interaction, and download-oriented approaches with tools that help users catalog, publish and visualize atmospheric data. Thus, we describe the architecture developed, based on the experience of the Atmospheric Radiation Measurement Data Center, which incorporates the principles of FAIR, the infrastructure and content management system for managing scientific data. The portal partial results were tested with environmental data from contaminated areas at the University of São Paulo. Overall, this data portal creates more shared knowledge about atmospheric processes by providing users with access to open environmental data.