Your search

COVID-19 has hit the world unprepared, as the deadliest pandemic of the century. Governments and authorities, as leaders and decision makers fighting the virus, enormously tap into the power of artificial intelligence and its predictive models for urgent decision support. This book showcases a collection of important predictive models that used during the pandemic, and discusses and compares their efficacy and limitations. Readers from both healthcare industries and academia can gain unique insights on how predictive models were designed and applied on epidemic data. Taking COVID19 as a case study and showcasing the lessons learnt, this book will enable readers to be better prepared in the event of virus epidemics or pandemics in the future.

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.

Continuous cardiac monitoring has been increasingly adopted to prevent heart diseases, especially the case of Chagas disease, a chronic condition that can degrade the heart condition, leading to sudden cardiac death. Unfortunately, a common challenge for these systems is the low-quality and high level of noise in ECG signal collection. Also, generic techniques to assess the ECG quality can discard useful information in these so-called chagasic ECG signals. To mitigate this issue, this work proposes a 1D CNN network to assess the quality of the ECG signal for chagasic patients and compare it to the state of art techniques. Segments of 10 s were extracted from 200 1-lead ECG Holter signals. Different feature extractions were considered such as morphological fiducial points, interval duration, and statistical features, aiming to classify 400 segments into four signal quality types: Acceptable ECG, Non-ECG, Wandering Baseline (WB), and AC Interference (ACI) segments. The proposed CNN architecture achieves a $$0.90 \pm 0.02$$accuracy in the multi-classification experiment and also $$0.94 \pm 0.01$$when considering only acceptable ECG against the other three classes. Also, we presented a complementary experiment showing that, after removing noisy segments, we improved morphological recognition (based on QRS wave) by 33% of the entire ECG data. The proposed noise detector may be applied as a useful tool for pre-processing chagasic ECG signals.

Consumers' selections and decision-making processes are some of the most exciting and challenging topics in neuromarketing, sales, and branding. From a global perspective, multicultural influences and societal conditions are crucial to consider. Neuroscience applications in international marketing and consumer behavior is an emergent and multidisciplinary field aiming to understand consumers' thoughts, reactions, and selection processes in branding and sales. This study focuses on real-time monitoring of different physiological signals using eye-tracking, facial expressions recognition, and Galvanic Skin Response (GSR) acquisition methods to analyze consumers' responses, detect emotional arousal, measure attention or relaxation levels, analyze perception, consciousness, memory, learning, motivation, preference, and decision-making. This research aimed to monitor human subjects' reactions to these signals during an experiment designed in three phases consisting of different branding advertisements. The nonadvertisement exposition was also monitored while gathering survey responses at the end of each phase. A feature extraction module with a data analytics module was implemented to calculate statistical metrics and decision-making supporting tools based on Principal Component Analysis (PCA) and Feature Importance (FI) determination based on the Random Forest technique. The results indicate that when compared to image ads, video ads are more effective in attracting consumers' attention and creating more emotional arousal.

The application of different tools for predicting COVID19 cases spreading has been widely considered during the pandemic. Comparing different approaches is essential to analyze performance and the practical support they can provide for the current pandemic management. This work proposes using the susceptible-exposed-asymptomatic but infectious-symptomatic and infectious-recovered-deceased (SEAIRD) model for different learning models. The first analysis considers an unsupervised prediction, based directly on the epidemiologic compartmental model. After that, two supervised learning models are considered integrating computational intelligence techniques and control engineering: the fuzzy-PID and the wavelet-ANN-PID models. The purpose is to compare different predictor strategies to validate a viable predictive control system for the COVID19 relevant epidemiologic time series. For each model, after setting the initial conditions for each parameter, the prediction performance is calculated based on the presented data. The use of PID controllers is justified to avoid divergence in the system when the learning process is conducted. The wavelet neural network solution is considered here because of its rapid convergence rate. The proposed solutions are dynamic and can be adjusted and corrected in real time, according to the output error. The results are presented in each subsection of the chapter.

A significant number of people infected by COVID19 do not get sick immediately but become carriers of the disease. These patients might have a certain incubation period. However, the classical compartmental model, SEIR, was not originally designed for COVID19. We used the simple, commonly used SEIR model to retrospectively analyse the initial pandemic data from Singapore. Here, the SEIR model was combined with the actual published Singapore pandemic data, and the key parameters were determined by maximizing the nonlinear goodness of fit R2 and minimizing the root mean square error. These parameters served for the fast and directional convergence of the parameters of an improved model. To cover the quarantine and asymptomatic variables, the existing SEIR model was extended to an infectious disease model with a greater number of population compartments, and with parameter values that were tuned adaptively by solving the nonlinear dynamics equations over the available pandemic data, as well as referring to previous experience with SARS. The contribution presented in this paper is a new model called the adaptive SEAIRD model; it considers the new characteristics of COVID19 and is therefore applicable to a population including asymptomatic carriers. The predictive value is enhanced by tuning of the optimal parameters, whose values better reflect the current pandemic.

There are several techniques to support simulation of time series behavior. In this chapter, the approach will be based on the Composite Monte Carlo (CMC) simulation method. This method is able to model future outcomes of time series under analysis from the available data. The establishment of multiple correlations and causality between the data allows modeling the variables and probabilistic distributions and subsequently obtaining also probabilistic results for time series forecasting. To improve the predictor efficiency, computational intelligence techniques are proposed, including a fuzzy inference system and an Artificial Neural Network architecture. This type of model is suitable to be considered not only for the disease monitoring and compartmental classes, but also for managerial data such as clinical resources, medical and health team allocation, and bed management, which are data related to complex decision-making challenges.

The COVID-19 pandemic spread generated an urgent need for computational systems to model its behavior and support governments and healthcare teams to make proper decisions. There are not many cases of global pandemics in history, and the most recent one has unique characteristics, which are tightly connected to the current society’s lifestyle and beliefs, creating an environment of uncertainty. Because of that, the development of mathematical/computational models to forecast the pandemic behavior since its beginning, i.e., with a restricted amount of data collected, is necessary. This chapter focuses on the analysis of different data mining techniques to allow the pandemic prediction with a small amount of data. A case study is presented considering the data from Wuhan, the Chinese city where the virus was first detected, and the place where the major outbreak occurred. The PNN + CF method (Polynomial Neural Network with Corrective Feedback) is presented as the technique with the best prediction performance. This is a promising method that might be considered in future eventual waves of the current pandemic or event to have a suitable model for future epidemic outbreaks around the world.

The gold standard to detect SARS-CoV-2 infection consider testing methods based on Polymerase Chain Reaction (PCR). Still, the time necessary to confirm patient infection can be lengthy, and the process is expensive. On the other hand, X-Ray and CT scans play a vital role in the auxiliary diagnosis process. Hence, a trusted automated technique for identifying and quantifying the infected lung regions would be advantageous. Chest X-rays are two-dimensional images of the patient’s chest and provide lung morphological information and other characteristics, like ground-glass opacities (GGO), horizontal linear opacities, or consolidations, which are characteristics of pneumonia caused by COVID-19. But before the computerized diagnostic support system can classify a medical image, a segmentation task should usually be performed to identify relevant areas to be analyzed and reduce the risk of noise and misinterpretation caused by other structures eventually present in the images. This chapter presents an AI-based system for lung segmentation in X-ray images using a U-net CNN model. The system’s performance was evaluated using metrics such as cross-entropy, dice coefficient, and Mean IoU on unseen data. Our study divided the data into training and evaluation sets using an 80/20 train-test split method. The training set was used to train the model, and the evaluation test set was used to evaluate the performance of the trained model. The results of the evaluation showed that the model achieved a Dice Similarity Coefficient (DSC) of 95%, Cross entropy of 97%, and Mean IoU of 86%.

Consumers' selections and decision-making processes are some of the most exciting and challenging topics in neuromarketing, sales, and branding. Multicultural influences and societal conditions are also crucial aspects to consider from a global perspective. Applying neuroscience tools and techniques in international marketing and consumer behavior is an emergent and multidisciplinary field that aims to understand consumers' thoughts, reactions, and selection processes in branding and sales. This study focuses on real-time monitoring of different physiological signals using eye-tracking, facial expressions recognition, and Galvanic Skin Response (GSR) acquisition methods to analyze consumers' responses, detect emotional arousal, measure attention or relaxation levels, analyze perception, consciousness, memory, learning, motivation, preference, and decision-making. The primary purpose of this research was to monitor human subjects' reactions to these signals during an experiment designed in three phases consisting of different types of branding advertisements. The non-advertisement exposition was also monitored during the gathering of survey responses at the end of each phase. A feature extraction module was implemented with a data analytics module to calculate statistical metrics and decision-making supporting tools based on Principal Component Analysis (PCA) and Feature Importance (FI) determination based on the Random Forest technique. The results indicate that when compared to image ads, video ads are more effective in attracting consumers' attention and creating more emotional arousal.

The identification of barriers for e-commerce to thrive in specific countries is a topic of great interest. This work proposes two models to study the barriers to B2C e-commerce adoption in Portugal, highlighting obstacles less exploited by previous research: the impact of offline shopping pleasure and the influence of the distance to shopping malls on online shopping intent. An online survey was conducted based on different constructs. A multivariate OLS hierarchical regression was used to analyse the proposed models regarding the intention to buy online and the number of online purchases. The results revealed that customer satisfaction is a strong predictor of intent to buy online and that perceived product risk remains a barrier to e-commerce. Consumers living in high urbanised areas have more propensity to buy online. Helpful information is provided regarding the impact of context, culture, product, and individual barriers, showing that multichannel strategies are best suited for success.

This paper presents an algorithm that applies metrics derived from automatic QRS detection and segmentation in electrocardiogram signals for analyzing Heart Rate Variability to study the evolution of metrics in the frequency domain of a clinical procedure. The analysis was performed on three sets of elderly people, who are categorized according to frailty phenotype. The first set was comprised of frail elderly, the second pre-frail elderly, and the third robust elderly. Investigators from many disciplines have been encouraged to contribute to the understanding of molecular and physiological changes in multiple systems that may increase the vulnerability of frail elderly. In this work, the frailty phenotype can be characterized by unintentional weight loss, as self-reported, fatigue assessed by self-report, grip strength (measured directly), physical activity level assessed by self-report and gait speed (measured). The results obtained demonstrate the existence of significant differences between Heart Rate Variability metrics for the three groups, especially considering a higher preponderance for sympathetic nervous system for the group of robust patients in response to postural maneuver.