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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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Covid-19 has hit the world unprepared, as the deadliest pandemic of the century. Governments and authorities, as leaders and decision makers fighting against the virus, enormously tap on the power of AI and its data analytics models for urgent decision supports at the greatest efforts, ever seen from human history. This book showcases a collection of important data analytics models that were used during the epidemic, and discusses and compares their efficacy and limitations. Readers who from both healthcare industries and academia can gain unique insights on how data analytics models were designed and applied on epidemic data. Taking Covid-19 as a case study, readers especially those who are working in similar fields, would be better prepared in case a new wave of virus epidemic may arise again in the near future.
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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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Starbucks Corporation (hereinafter “Starbucks” or “the Company”) is a worldwide coffee retailer, which operates over 33,000 stores located in over 83 countries nowadays. The purpose of the study is to estimate Starbucks’ intrinsic value as of December 31, 2021 and identify whether the Company was overvalued or undervalued. Several analyses give investors and shareholders an insight into how the Company may develop or identify the ability to generate positive returns from investing in Starbucks. This study is mainly separated into two aspects. The first part specifically discusses the Company overview, industry analysis, and economic outlook, which includes SWOT analysis, PESTEL analysis, Porter's five forces analysis, and value chain analysis to identify external and internal factors that may influence the Company. The second part focuses on financial analyzes, including both historical and forecasted financial statement. Three valuation models and a sensitive analysis are applied to understand the Company’s financial conditions and performance. Starbucks’ intrinsic value is derived from the three discounted cash flow models, indicating the market overvalued the Company’s stock price as of December 31, 2021. Finally, investors and shareholders can understand more about Starbucks’ capital structure, financial highlights, and intrinsic value, because this set of information is critical for existing investors and potential investors to make investment decisions
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Limited special education and related services are available for children with autism spectrum disorder (ASD) in Macau, especially those who are educated in general education classrooms. No intervention study has been conducted on these children. This study was conducted to explore the relationship between a board game play intervention and board game play behaviors and social communication of children with ASD educated in general education classrooms in Macau. A repeated measures design was used and the results of this study showed the mean occurrence of unprompted board game play behaviors per session during intervention was not significantly different from that during pre- or post-intervention. The mean occurrence of social communication per session during intervention was significantly higher than that during pre- and post-intervention. These findings suggest a positive relationship existed between the board game intervention used in this study and social communication of children with ASD.
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Esta dissertação reporta uma investigação realizada com o objectivo de se compreender a persistente dificuldade de integração sentida no patamar transnacional de um projeto educacional europeu Comenius, de parceria entre escolas. A Grounded Theory foi a metodologia selecionada para orientar a recolha e análise de dados. Os dados empíricos primários foram extraídos de entrevistas abertas, não estruturadas, realizadas aos professores europeus envolvidos nas atividades do projeto. Identificou-se, como principal fator obstrutivo da unidade operacional a esse nível no projeto, a existência de significativas barreiras à comunicação entre os diferentes parceiros europeus, latentes, pouco compreensíveis, mas extraordinariamente operativas. Implicações do estudo e sugestões para esforços subsequentes visando evitar ou, pelo menos, contornar o mesmo tipo de problema, ou problemas similares, estão incluídas
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Hong Kong and Macau were politically reunified with Mainland China in 1997 and 1999, respectively. These two cities culturally originated from Mainland China, but due to their own colonial experiences, the Chinese cultural identities within Hong Kong, Macau, and Mainland China became different. The nature of Chinese cultural identities within Hong Kong and Macau were hybridized, and they have formed their own Chinese cultural identities with their own peculiarities. The Internet is a popular communication medium and it facilitates cultural communication inside and outside of these three places. The high-speed development of modern technology leads to the variety of services that emerge in the Internet, such as discussion forum, Blog, Facebook, Twitter, etc. These new and open spaces serve as a platform for ordinary people to express themselves in different ways. General observations in the Internet reveal that the discussion on Chinese cultural identity among Hong Kong, Macau, and Mainland China exists. The combination of self-identity and reconstruction of self-cultural identity are happening in these differently colonized places. Some local Chinese people in Macau, and Taiwanese in Taiwan, share this kind of experience as well. Meanings in different issues via different symbols are formed and they can be seen from the photos that circulate in the Internet using threads posted in Blogs or discussion forums. All these kinds of images or contexts become symbols of recognizable identities. Internet use, therefore, has facilitated the cultural communication between Hong Kong, Macau, Mainland China, and Taiwan. It has also intensified the enlightenment of Chinese cultural identity, showing and highlighting in effect the remnants of recognizable traits in these territories that were once colonized by different states. In essence, they may arguably have formed heterogeneous Chinese cultural identities. This study presents the uniqueness of the formation of Macau identity in comparison to Hong Kong, and how different it was from Hong Kong after the end of the colonial period. This ‘awakening process’, it is argued, provides a new perspective for understanding the attendant connotations and evaluations of cultural identities, and the different perspectives used to understand how the Internet is reshaping the social world. The reconstruction of cultural identity is a global issue and cultural hybridity is an essential element for reconstruction of self-cultural identity in the postcolonial period. This study employs postcolonial theory, along with observation, in-depth interview and online data collection and content analysis that were adapted during the course of the research, in order to discuss this phenomenon
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