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The area of clinical decision support systems (CDSS) is facing a boost in research and development with the increasing amount of data in clinical analysis together with new tools to support patient care. This creates a vibrant and challenging environment for the medical and technical staff. This chapter presents a discussion about the challenges and trends of CDSS considering big data and patient-centered constraints. Two case studies are presented in detail. The first presents the development of a big data and AI classification system for maternal and fetal ambulatory monitoring, composed by different solutions such as the implementation of an Internet of Things sensors and devices network, a fuzzy inference system for emergency alarms, a feature extraction model based on signal processing of the fetal and maternal data, and finally a deep learning classifier with six convolutional layers achieving an F1-score of 0.89 for the case of both maternal and fetal as harmful. The system was designed to support maternal–fetal ambulatory premises in developing countries, where the demand is extremely high and the number of medical specialists is very low. The second case study considered two artificial intelligence approaches to providing efficient prediction of infections for clinical decision support during the COVID-19 pandemic in Brazil. First, LSTM recurrent neural networks were considered with the model achieving R2=0.93 and MAE=40,604.4 in average, while the best, R2=0.9939, was achieved for the time series 3. Second, an open-source framework called H2O AutoML was considered with the “stacked ensemble” approach and presented the best performance followed by XGBoost. Brazil has been one of the most challenging environments during the pandemic and where efficient predictions may be the difference in saving lives. The presentation of such different approaches (ambulatory monitoring and epidemiology data) is important to illustrate the large spectrum of AI tools to support clinical decision-making.
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The global pandemic triggered by the Corona Virus Disease firstly detected in 2019 (COVID-19), entered the fourth year with many unknown aspects that need to be continuously studied by the medical and academic communities. According to the World Health Organization (WHO), until January 2023, more than 650 million cases were officially accounted (with probably much more non tested cases) with 6,656,601 deaths officially linked to the COVID-19 as plausible root cause. In this Chapter, an overview of some relevant technical aspects related to the COVID-19 pandemic is presented, divided in three parts. First, the advances are highlighted, including the development of new technologies in different areas such as medical devices, vaccines, and computerized system for medical support. Second, the focus is on relevant challenges, including the discussion on how computerized diagnostic supporting systems based on Artificial Intelligence are in fact ready to effectively help on clinical processes, from the perspective of the model proposed by NASA, Technology Readiness Levels (TRL). Finally, two trends are presented with increased necessity of computerized systems to deal with the Long Covid and the interest on Precision Medicine digital tools. Analyzing these three aspects (advances, challenges, and trends) may provide a broader understanding of the impact of the COVID-19 pandemic on the development of Computerized Diagnostic Support Systems.
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The scientific literature indicates that pregnant women with COVID-19 are at an increased risk for developing more severe illness conditions when compared with non-pregnant women. The risk of admission to an ICU (Intensive Care Unit) and the need for mechanical ventilator support is three times higher. More significantly, statistics indicate that these patients are also at 70% increased risk of evolving to severe states or even death. In addition, other previous illnesses and age greater than 35 years old increase the risk for the mother and the fetus, including a higher number of cesarean sections, higher systolic and diastolic maternal blood pressure, increasing the risk of eclampsia, and, in some cases, preterm birth. Additionally, pregnant women have more Emotional lability/fluctuations (between positive and negative feelings) during the entire pregnancy. The emotional instability and brain fog that takes place during gestation may open vulnerability for neuropsychiatric symptoms of long COVID, which this population was not studied in depth. The present Chapter characterizes the database presented in this work with clinical and survey data collected about emotions and feelings using the Coronavirus Perinatal Experiences—Impact Survey (COPE-IS). Pregnant women with or without COVID-19 symptoms who gave birth at the Assis Chateaubriand Maternity Hospital (MEAC), a public maternity of the Federal University of Ceara, Brazil, were recruited. In total, 72 mother-infant dyads were included in the study and are considered in this exploratory analysis. The participants have undergone serological tests for SARS-CoV-2 antibody detection and a nasopharyngeal swab test for COVID-19 diagnoses by RT-PCR. A comprehensive Exploratory Data Analysis (EDA) is performed using frequency distribution analysis of multiple types of variables generated from numerical data, multiple-choice, categorized, and Likert-scale questions.
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At the beginning of 2020, the World Health Organization (WHO) started a coordinated global effort to counterattack the potential exponential spread of the SARS-Cov2 virus, responsible for the coronavirus disease, officially named COVID-19. This comprehensive initiative included a research roadmap published in March 2020, including nine dimensions, from epidemiological research to diagnostic tools and vaccine development. With an unprecedented case, the areas of study related to the pandemic received funds and strong attention from different research communities (universities, government, industry, etc.), resulting in an exponential increase in the number of publications and results achieved in such a small window of time. Outstanding research cooperation projects were implemented during the outbreak, and innovative technologies were developed and improved significantly. Clinical and laboratory processes were improved, while managerial personnel were supported by a countless number of models and computational tools for the decision-making process. This chapter aims to introduce an overview of this favorable scenario and highlight a necessary discussion about ethical issues in research related to the COVID-19 and the challenge of low-quality research, focusing only on the publication of techniques and approaches with limited scientific evidence or even practical application. A legacy of lessons learned from this unique period of human history should influence and guide the scientific and industrial communities for the future.
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The adoption of computer-aided diagnosis and treatment systems based on different types of artificial neural networks (ANNs) is already a reality in several hospital and ambulatory premises. This chapter aims to present a discussion focused on the challenges and trends of adopting these computerized systems, highlighting solutions based on different types and approaches of ANN, more specifically, feed-forward, recurrent, and deep convolutional architectures. One section is focused on the application of AI/ANN solutions to support cardiology in different applications, such as the classification of the heart structure and functional behavior based on echocardiography images; the automatic analysis of the heart electric activity based on ECG signals; and the diagnosis support of angiogram images during surgical interventions. Finally, a case study is presented based on the application of a deep learning convolutional network together with a recent technique called transfer learning to detect brain tumors using an MRI images data set. According to the findings, the model has a high degree of specificity (precision of 0.93 and recall of 0.94 for images with no brain tumor) and can be used as a screening tool for images that do not contain a brain tumor. The f1-score for images with brain tumor was 0.93. The results achieved are very promising and the proposed solution may be considered to be used as a computer-aided diagnosis tool based on deep learning convolutional neural networks. Future works will consider other techniques and compare them with the one presented here. With the comprehensive approach and overview of multiple applications, it is valid to conclude that computer-aided diagnosis and treatment systems are important tools to be considered today and will be an essential part of the trend of personalized medicine over the coming years.
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In his most quoted study Imagined Communities, Benedict Anderson argues that the invention of the printing press and the rise of print media contributed to a textual representation of the concept of the nation and nationalism. He states that ‘popular’ print culture was also crucial in its contribution to a global exchange that would have reinforced the idea of an ‘imagined community’.1 Anderson further explains that before the eighteenth century, the concept of nation was extensive, as Latin was the language of a broad, vast, imagined community called ‘Christendom’, but as there were changes in the religious communities, such a concept began to be replaced by French and English as vernacular languages of administrative centralization.2 Thus, print capitalism allied to the book market supported by the improvement of communications and the emergence of new and diverse forms of national languages, originated the creation of clusters of small creole ‘imagined political communities’ that were eager to promote new forms of national and cultural consciousness, aimed at widespread literacy through liens of kinship, ethnicity, fraternity, and power loyalties.3 This chapter posits that Anderson's arguments regarding creole nationalism in the new world, fit the particular case of the emergence of the printing, publishing and book-selling culture among a Euro-creole bourgeoisie from Macao with solid kinship, ethnic, commercial and social connections in Hong Kong, Canton, Shanghai and other littoral spaces in the treaty ports in East Asia, and takes these developments as a necessary point of departure. I argue that they used the widespread nature of print media to empower themselves and other community members with the progressive eighteenth-century Enlightenment ideas on rational scientific knowledge. They embraced atheism and anti-clericalism as important elements of enlightenment, thus promoting scientific culture, constitutional monarchy or republican forms of government, social mobility for ethnic minorities, and religious and intellectual tolerance that to a certain extent challenged the Catholic Church and conservative circles.
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Born in Magule, Mozambique (Portuguese East Africa), João dos Santos Albasini was a mulatto, the leading intellectual in the main center, Lourenço Marques (today, Maputo), the editor of O Africano (The African) founded 1908, and O Brado Africano (The African Voice) founded in 1918, and a champion of worker and African rights. Often characterized as a republican and a moderate, Albasini had a basic education and an appetite for ideas. He was an avid reader of republican theory, syndicalism, and anarchism – all influential in Portugal – and was familiar with a range of radical ideas circulating in the city's thriving café culture.
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Collecting more than 200 sources in the global history of feminism, this anthology supplies an insightful record of the resistance to patriarchy throughout human history and around the world.From writings by Enheduana in ancient Mesopotamia (2350 BCE) to the present-day manifesto of the Association of Women for Action and Research in Singapore, Feminist Writings from Ancient Times to the Modern World: A Global Sourcebook and History excerpts more than 200 feminist primary source documents from Africa to the Americas to Australia.Serving to depict "feminism" as much broader—and older—than simply the modern struggle for political rights and equality, this two-volume work provides a more comprehensive and varied record of women's resistance cross-culturally and throughout history. The author's goal is to showcase a wide range of writers, thinkers, and organizations in order to document how resistance to patriarchy has been at the center of social, political, and intellectual history since the infancy of human civilization. This work addresses feminist ideas expressed privately through poetry, letters, and autobiographies, as well as the public and political aspects of women's rights movements.More than 200 chronologically arranged entries on feminist writers, thinkers, and organizations across 4,000 years of human historyContributions from more than 100 international scholars, including historians, sociologists, literary, cultural theorists, religious scholars, writers, and activistsBrief bibliographies of further readings, websites, and other relevant resources with each entryLists of entries arranged by region as well as by broad topic, in addition to a comprehensive index