TY - CHAP TI - X-Ray Machine Learning Classification with VGG-16 for Feature Extraction AU - dos Santos Silva, Bruno Riccelli AU - Cortez, Paulo Cesar AU - da Silva Neto, Manuel Gonçalves AU - Lobo Marques, Joao Alexandre T2 - Computerized Systems for Diagnosis and Treatment of COVID-19 A2 - Lobo Marques, Joao Alexandre A2 - Fong, Simon James AB - The Covid-19 pandemic evidenced the need Computer Aided Diagnostic Systems to analyze medical images, such as CT and MRI scans and X-rays, to assist specialists in disease diagnosis. CAD systems have been shown to be effective at detecting COVID-19 in chest X-ray and CT images, with some studies reporting high levels of accuracy and sensitivity. Moreover, it can also detect some diseases in patients who may not have symptoms, preventing the spread of the virus. There are some types of CAD systems, such as Machine and Deep Learning-based and Transfer learning-based. This chapter proposes a pipeline for feature extraction and classification of Covid-19 in X-ray images using transfer learning for feature extraction with VGG-16 CNN and machine learning classifiers. Five classifiers were evaluated: Accuracy, Specificity, Sensitivity, Geometric mean, and Area under the curve. The SVM Classifier presented the best performance metrics for Covid-19 classification, achieving 90% accuracy, 97.5% of Specificity, 82.5% of Sensitivity, 89.6% of Geometric mean, and 90% for the AUC metric. On the other hand, the Nearest Centroid (NC) classifier presented poor sensitivity and geometric mean results, achieving 33.9% and 54.07%, respectively. CY - Cham DA - 2023/// PY - 2023 DP - Springer Link SP - 65 EP - 78 LA - en PB - Springer International Publishing SN - 978-3-031-30788-1 UR - https://doi.org/10.1007/978-3-031-30788-1_5 Y2 - 2023/10/10/04:37:07 ER - TY - CHAP TI - TPOT Automated Machine Learning Approach for Multiple Diagnostic Classification of Lung Radiography and Feature Extraction AU - Bernardo Gois, Francisco Nauber AU - Lobo Marques, Joao Alexandre AU - Fong, Simon James T2 - Computerized Systems for Diagnosis and Treatment of COVID-19 A2 - Lobo Marques, Joao Alexandre A2 - Fong, Simon James AB - This chapter describes an AUTO-ML strategy to detect COVID on chest X-rays utilizing Transfer Learning feature extraction and the AutoML TPOT framework in order to identify lung illnesses (such as COVID or pneumonia). MobileNet is a lightweight network that uses depthwise separable convolution to deepen the network while decreasing parameters and computation. AutoML is a revolutionary concept of automated machine learning (AML) that automates the process of building an ML pipeline inside a constrained computing framework. The term “AutoML” can mean a number of different things depending on context. AutoML has risen to prominence in both the business world and the academic community thanks to the ever-increasing capabilities of modern computers. Python Optimised ML Pipeline (TPOT) is a Python-based ML tool that optimizes pipeline efficiency via genetic programming. We use TPOT builds models for extracted MobileNet network features from COVID-19 image data. The f1-score of 0.79 classifies Normal, Viral Pneumonia, and Lung Opacity. CY - Cham DA - 2023/// PY - 2023 DP - Springer Link SP - 117 EP - 135 LA - en PB - Springer International Publishing SN - 978-3-031-30788-1 UR - https://doi.org/10.1007/978-3-031-30788-1_8 Y2 - 2023/10/10/04:37:12 ER - TY - CHAP TI - The Comparison of Different Linear and Nonlinear Models Using Preliminary Data to Efficiently Analyze the COVID-19 Outbreak AU - Fong, Simon James AU - Marques, João Alexandre Lobo AU - Li, G. AU - Dey, N. AU - Crespo, Rubén G. AU - Herrera-Viedma, E. AU - Gois, F. Nauber Bernardo AU - Neto, José Xavier T2 - Epidemic Analytics for Decision Supports in COVID19 Crisis A2 - Marques, Joao Alexandre Lobo A2 - Fong, Simon James AB - 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. CY - Cham DA - 2022/// PY - 2022 DP - Springer Link SP - 65 EP - 81 LA - en PB - Springer International Publishing SN - 978-3-030-95281-5 UR - https://doi.org/10.1007/978-3-030-95281-5_3 Y2 - 2022/09/21/02:36:15 KW - Artificial neural networks KW - Epidemiology KW - Machine learning KW - Prediction models ER - TY - CHAP TI - The Application of Supervised and Unsupervised Computational Predictive Models to Simulate the COVID19 Pandemic AU - Fong, Simon James AU - Marques, João Alexandre Lobo AU - Li, Gloria AU - Dey, Nilanjan AU - Crespo, Rubén González AU - Herrera-Viedma, Enrique AU - Gois, Francisco Nauber Bernardo AU - Neto, José Xavier T2 - Epidemic Analytics for Decision Supports in COVID19 Crisis A2 - Marques, Joao Alexandre Lobo A2 - Fong, Simon James AB - 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. CY - Cham DA - 2022/// PY - 2022 DP - Springer Link SP - 103 EP - 139 LA - en PB - Springer International Publishing SN - 978-3-030-95281-5 UR - https://doi.org/10.1007/978-3-030-95281-5_5 Y2 - 2022/09/21/02:33:30 KW - ANN predictor KW - COVID19 KW - Epidemiology KW - Fuzzy predictor KW - PID control KW - SEAIRD ER - TY - CHAP TI - Technology Developments to Face the COVID-19 Pandemic: Advances, Challenges, and Trends AU - Lobo Marques, Joao Alexandre AU - Fong, Simon James T2 - Computerized Systems for Diagnosis and Treatment of COVID-19 A2 - Lobo Marques, Joao Alexandre A2 - Fong, Simon James AB - 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. CY - Cham DA - 2023/// PY - 2023 DP - Springer Link SP - 1 EP - 13 LA - en PB - Springer International Publishing SN - 978-3-031-30788-1 ST - Technology Developments to Face the COVID-19 Pandemic UR - https://doi.org/10.1007/978-3-031-30788-1_1 Y2 - 2023/10/10/04:37:00 ER - TY - CHAP TI - Segmentation of CT-Scan Images Using UNet Network for Patients Diagnosed with COVID-19 AU - Bernardo Gois, Francisco Nauber AU - Lobo Marques, Joao Alexandre T2 - Computerized Systems for Diagnosis and Treatment of COVID-19 A2 - Lobo Marques, Joao Alexandre A2 - Fong, Simon James AB - The use of computational tools for medical image processing are promising tools to effectively detect COVID-19 as an alternative to expensive and time-consuming RT-PCR tests. For this specific task, CXR (Chest X-Ray) and CCT (Chest CT Scans) are the most common examinations to support diagnosis through radiology analysis. With these images, it is possible to support diagnosis and determine the disease’s severity stage. Computerized COVID-19 quantification and evaluation require an efficient segmentation process. Essential tasks for automatic segmentation tools are precisely identifying the lungs, lobes, bronchopulmonary segments, and infected regions or lesions. Segmented areas can provide handcrafted or self-learned diagnostic criteria for various applications. This Chapter presents different techniques applied for Chest CT Scans segmentation, considering the state of the art of UNet networks to segment COVID-19 CT scans and a segmentation experiment for network evaluation. Along 200 epochs, a dice coefficient of 0.83 was obtained. CY - Cham DA - 2023/// PY - 2023 DP - Springer Link SP - 29 EP - 44 LA - en PB - Springer International Publishing SN - 978-3-031-30788-1 UR - https://doi.org/10.1007/978-3-031-30788-1_3 Y2 - 2023/10/10/04:37:03 ER - TY - CHAP TI - Research and Technology Development Achievements During the COVID-19 Pandemic—An Overview AU - Marques, João Alexandre Lobo AU - Fong, Simon James AU - Li, G. AU - Arraut, Ivan AU - Gois, F. Nauber Bernardo AU - Neto, José Xavier T2 - Epidemic Analytics for Decision Supports in COVID19 Crisis A2 - Marques, Joao Alexandre Lobo A2 - Fong, Simon James AB - 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. CY - Cham DA - 2022/// PY - 2022 DP - Springer Link SP - 1 EP - 15 LA - en PB - Springer International Publishing SN - 978-3-030-95281-5 UR - https://doi.org/10.1007/978-3-030-95281-5_1 Y2 - 2022/09/21/02:35:32 KW - COVID-19 KW - Research cooperation KW - Research ethics KW - Scientific research KW - Technology development ER - TY - CHAP TI - Probabilistic Forecasting Model for the COVID-19 Pandemic Based on the Composite Monte Carlo Model Integrated with Deep Learning and Fuzzy System AU - Fong, Simon James AU - Marques, João Alexandre Lobo AU - Li, Gloria AU - Dey, Nilanjan AU - Crespo, Rubén González AU - Herrera-Viedma, Enrique AU - Gois, Francisco Nauber Bernardo AU - Neto, José Xavier T2 - Epidemic Analytics for Decision Supports in COVID19 Crisis A2 - Marques, Joao Alexandre Lobo A2 - Fong, Simon James AB - 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. CY - Cham DA - 2022/// PY - 2022 DP - Springer Link SP - 83 EP - 102 LA - en PB - Springer International Publishing SN - 978-3-030-95281-5 UR - https://doi.org/10.1007/978-3-030-95281-5_4 Y2 - 2022/09/21/02:35:35 KW - COVID-19 KW - Composite Monte Carlo simulation KW - Healthcare decision-making systems KW - Prediction ER - TY - BOOK TI - Predictive Models for Decision Support in the COVID-19 Crisis AU - Marques, João Alexandre Lobo AU - Gois, Francisco Nauber Bernardo AU - Xavier-Neto, Jose AU - Fong, Simon James T2 - SpringerBriefs in Applied Sciences and Technology AB - 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. DA - 2021/// PY - 2021 LA - en PB - Springer International Publishing SN - 978-3-030-61912-1 UR - https://www.springer.com/gp/book/9783030619121 Y2 - 2021/01/29/07:53:53 ER - TY - CHAP TI - Prediction for Decision Support During the COVID-19 Pandemic AU - Marques, Joao Alexandre Lobo AU - Gois, Francisco Nauber Bernardo AU - Xavier-Neto, José AU - Fong, Simon James T2 - Predictive Models for Decision Support in the COVID-19 Crisis T3 - Predictive Models for Decision Support in the COVID-19 Crisis DA - 2020/12// PY - 2020 DP - ORCID SP - 1 EP - 13 PB - Springer International Publishing UR - https://doi.org/10.1007%2F978-3-030-61913-8_1 Y2 - 2021/02/03/09:10:21 ER - TY - CHAP TI - Predicting the Geographic Spread of the COVID-19 Pandemic: A Case Study from Brazil AU - Marques, Joao Alexandre Lobo AU - Gois, Francisco Nauber Bernardo AU - Xavier-Neto, José AU - Fong, Simon James T2 - Predictive Models for Decision Support in the COVID-19 Crisis T3 - Predictive Models for Decision Support in the COVID-19 Crisis DA - 2020/12// PY - 2020 DP - ORCID SP - 89 EP - 98 PB - Springer International Publishing ST - Predicting the Geographic Spread of the COVID-19 Pandemic UR - https://doi.org/10.1007%2F978-3-030-61913-8_6 Y2 - 2021/02/03/09:10:21 ER - TY - CHAP TI - Nonlinear Prediction for the COVID-19 Data Based on Quadratic Kalman Filtering AU - Marques, Joao Alexandre Lobo AU - Gois, Francisco Nauber Bernardo AU - Xavier-Neto, José AU - Fong, Simon James T2 - Predictive Models for Decision Support in the COVID-19 Crisis T3 - Predictive Models for Decision Support in the COVID-19 Crisis DA - 2020/12// PY - 2020 DP - ORCID SP - 55 EP - 68 PB - Springer International Publishing UR - https://doi.org/10.1007%2F978-3-030-61913-8_4 Y2 - 2021/02/03/09:10:21 ER - TY - CHAP TI - Lung Segmentation of Chest X-Rays Using Unet Convolutional Networks AU - dos Santos Silva, Bruno Riccelli AU - Cesar Cortez, Paulo AU - Gomes Aguiar, Rafael AU - Rodrigues Ribeiro, Tulio AU - Pereira Teixeira, Alexandre AU - Bernardo Gois, Francisco Nauber AU - Lobo Marques, Joao Alexandre T2 - Computerized Systems for Diagnosis and Treatment of COVID-19 A2 - Lobo Marques, Joao Alexandre A2 - Fong, Simon James AB - 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%. CY - Cham DA - 2023/// PY - 2023 DP - Springer Link SP - 15 EP - 28 LA - en PB - Springer International Publishing SN - 978-3-031-30788-1 UR - https://doi.org/10.1007/978-3-031-30788-1_2 Y2 - 2023/10/10/04:41:11 ER - TY - CHAP TI - Forecasting COVID-19 Time Series Based on an Autoregressive Model AU - Marques, Joao Alexandre Lobo AU - Gois, Francisco Nauber Bernardo AU - Xavier-Neto, José AU - Fong, Simon James T2 - Predictive Models for Decision Support in the COVID-19 Crisis T3 - Predictive Models for Decision Support in the COVID-19 Crisis DA - 2020/12// PY - 2020 DP - ORCID SP - 41 EP - 54 PB - Springer International Publishing UR - https://doi.org/10.1007%2F978-3-030-61913-8_3 Y2 - 2021/02/03/09:10:21 ER - TY - CHAP TI - Exploratory Data Analysis on Clinical and Emotional Parameters of Pregnant Women with COVID-19 Symptoms AU - Lobo Marques, Joao Alexandre AU - Macedo, Danielle S. AU - Motta, Pedro AU - dos Santos Silva, Bruno Riccelli AU - Carvalho, Francisco Herlanio Costa AU - Kehdi, Renata Castro AU - Cavalcante, Letícia Régia Lima AU - da Silva Viana, Marylane AU - Lós, Deniele AU - Fiorenza, Natália Gindri T2 - Computerized Systems for Diagnosis and Treatment of COVID-19 A2 - Lobo Marques, Joao Alexandre A2 - Fong, Simon James AB - 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. CY - Cham DA - 2023/// PY - 2023 DP - Springer Link SP - 179 EP - 209 LA - en PB - Springer International Publishing SN - 978-3-031-30788-1 UR - https://doi.org/10.1007/978-3-031-30788-1_11 Y2 - 2023/10/10/04:37:22 ER - TY - CHAP TI - Evaluation of ECG Non-linear Features in Time-Frequency Domain for the Discrimination of COVID-19 Severity Stages AU - Ribeiro, Pedro AU - Pordeus, Daniel AU - Zacarias, Laíla AU - Leite, Camila AU - Alves Neto, Manoel AU - Aires Peixoto Jr, Arnaldo AU - de Oliveira, Adriel AU - Paulo Madeiro, João AU - Lobo Marques, Joao Alexandre AU - Miguel Rodrigues, Pedro T2 - Computerized Systems for Diagnosis and Treatment of COVID-19 A2 - Lobo Marques, Joao Alexandre A2 - Fong, Simon James AB - In 2020, the World Health Organization declared the Coronavirus Disease 19 a global pandemic. While detecting COVID-19 is essential in controlling the disease, prognosis prediction is crucial in reducing disease complications and patient mortality. For that, standard protocols consider adopting medical imaging tools to analyze cases of pneumonia and complications. Nevertheless, some patients develop different symptoms and/or cannot be moved to a CT-Scan room. In other cases, the devices are not available. The adoption of ambulatory monitoring examinations, such as Electrocardiography (ECG), can be considered a viable tool to address the patient’s cardiovascular condition and to act as a predictor for future disease outcomes. In this investigation, ten non-linear features (Energy, Approximate Entropy, Logarithmic Entropy, Shannon Entropy, Hurst Exponent, Lyapunov Exponent, Higuchi Fractal Dimension, Katz Fractal Dimension, Correlation Dimension and Detrended Fluctuation Analysis) extracted from 2 ECG signals (collected from 2 different patient’s positions). Windows of 1 second segments in 6 ways of windowing signal analysis crops were evaluated employing statistical analysis. Three categories of outcomes are considered for the patient status: Low, Moderate, and Severe, and four combinations for classification scenarios are tested:  (Low vs. Moderate, Low vs. Severe, Moderate vs. Severe) and 1 Multi-class comparison  (All vs. All)). The results indicate that some statistically significant parameter distributions were found for all comparisons.  (Low vs. Moderate—Approximate Entropy p-value = 0.0067 < 0.05, Low vs. Severe—Correlation Dimension p-value = 0.0087 < 0.05, Moderate vs. Severe—Correlation Dimension p-value = 0.0029 < 0.05, All vs. All—Correlation Dimension p-value = 0.0185 < 0.05. The non-linear analysis of the time-frequency representation of the ECG signal can be considered a promising tool for describing and distinguishing the COVID-19 severity activity along its different stages. CY - Cham DA - 2023/// PY - 2023 DP - Springer Link SP - 137 EP - 154 LA - en PB - Springer International Publishing SN - 978-3-031-30788-1 UR - https://doi.org/10.1007/978-3-031-30788-1_9 Y2 - 2023/10/10/04:41:02 KW - COVID-19 KW - ECG signals KW - Non-linear analysis KW - Statistical analysis ER - TY - CHAP TI - Epidemiology Compartmental Models—SIR, SEIR, and SEIR with Intervention AU - Marques, Joao Alexandre Lobo AU - Gois, Francisco Nauber Bernardo AU - Xavier-Neto, José AU - Fong, Simon James T2 - Predictive Models for Decision Support in the COVID-19 Crisis T3 - Predictive Models for Decision Support in the COVID-19 Crisis DA - 2020/12// PY - 2020 DP - ORCID SP - 15 EP - 39 PB - Springer International Publishing UR - https://doi.org/10.1007%2F978-3-030-61913-8_2 Y2 - 2021/02/03/09:10:21 ER - TY - JOUR TI - COVID-19: Psychological and Psychosocial Impact, Fear, and Passion AU - Dey, Nilanjan AU - Mishra, Rishabh AU - Fong, Simon James AU - Santosh, K. C. AU - Tan, Stanna AU - Crespo, Rubén González T2 - Digital Government: Research and Practice DA - 2021/01/31/ PY - 2021 DO - 10.1145/3428088 DP - DOI.org (Crossref) VL - 2 IS - 1 SP - 1 EP - 4 J2 - Digit. Gov.: Res. Pract. LA - en SN - 2691-199X, 2639-0175 ST - COVID-19 UR - https://dl.acm.org/doi/10.1145/3428088 Y2 - 2023/04/11/13:48:11 ER - TY - CHAP TI - Covid-19 Detection Based on Chest X-Ray Images Using Multiple Transfer Learning CNN Models AU - dos Santos Silva, Bruno Riccelli AU - Cesar Cortez, Paulo AU - Crosara Motta, Pedro AU - Lobo Marques, Joao Alexandre T2 - Computerized Systems for Diagnosis and Treatment of COVID-19 A2 - Lobo Marques, Joao Alexandre A2 - Fong, Simon James AB - The gold standard to detect SARS-CoV-2 infection considers testing methods based on Polymerase Chain Reaction (PCR). Still, the time necessary to confirm patient infection can be lengthy, and the process is expensive. In parallel, X-Ray and CT scans play an important role in the diagnosis and treatment processes. 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 typical characteristics of pneumonia caused by COVID-19. This chapter presents an AI-based system using multiple Transfer Learning models for COVID-19 classification using Chest X-Rays. In our experimental design, all the classifiers demonstrated satisfactory accuracy, precision, recall, and specificity performance. On the one hand, the Mobilenet architecture outperformed the other CNNs, achieving excellent results for the evaluated metrics. On the other hand, Squeezenet presented a regular result in terms of recall. In medical diagnosis, false negatives can be particularly harmful because a false negative can lead to patients being incorrectly diagnosed as healthy. These results suggest that our Deep Learning classifiers can accurately classify X-ray exams as normal or indicative of COVID-19 with high confidence. CY - Cham DA - 2023/// PY - 2023 DP - Springer Link SP - 45 EP - 63 LA - en PB - Springer International Publishing SN - 978-3-031-30788-1 UR - https://doi.org/10.1007/978-3-031-30788-1_4 Y2 - 2023/10/10/04:37:05 ER - TY - CHAP TI - COVID-19 Classification Using CT Scans with Convolutional Neural Networks AU - Motta, Pedro Crosara AU - Cesar Cortez, Paulo AU - Lobo Marques, Jao Alexandre T2 - Computerized Systems for Diagnosis and Treatment of COVID-19 A2 - Lobo Marques, Joao Alexandre A2 - Fong, Simon James AB - Even with more than 12 billion vaccine doses administered globally, the Covid-19 pandemic has caused several global economic, social, environmental, and healthcare impacts. Computer Aided Diagnostic (CAD) systems can serve as a complementary method to aid doctors in identifying regions of interest in images and help detect diseases. In addition, these systems can help doctors analyze the status of the disease and check for their progress or regression. To analyze the viability of using CNNs for differentiating Covid-19 CT positive images from Covid-19 CT negative images, we used a dataset collected by Union Hospital (HUST-UH) and Liyuan Hospital (HUST-LH) and made available at the Kaggle platform. The main objective of this chapter is to present results from applying two state-of-the-art CNNs on a Covid-19 CT Scan images database to evaluate the possibility of differentiating images with imaging features associated with Covid-19 pneumonia from images with imaging features irrelevant to Covid-19 pneumonia. Two pre-trained neural networks, ResNet50 and MobileNet, were fine-tuned for the datasets under analysis. Both CNNs obtained promising results, with the ResNet50 network achieving a Precision of 0.97, a Recall of 0.96, an F1-score of 0.96, and 39 false negatives. The MobileNet classifier obtained a Precision of 0.94, a Recall of 0.94, an F1-score of 0.94, and a total of 20 false negatives. CY - Cham DA - 2023/// PY - 2023 DP - Springer Link SP - 99 EP - 116 LA - en PB - Springer International Publishing SN - 978-3-031-30788-1 UR - https://doi.org/10.1007/978-3-031-30788-1_7 Y2 - 2023/10/11/10:42:48 ER - TY - BOOK TI - Computerized Systems for Diagnosis and Treatment of COVID-19 A3 - Lobo Marques, Joao Alexandre A3 - Fong, Simon James CY - Cham DA - 2023/// PY - 2023 DP - DOI.org (Crossref) LA - en PB - Springer International Publishing SN - 978-3-031-30787-4 978-3-031-30788-1 UR - https://link.springer.com/10.1007/978-3-031-30788-1 Y2 - 2023/10/10/04:35:42 KW - Artificial Intelligence KW - Biofeedback KW - Computerized Diagnostic Support KW - Covid-19 KW - Signal and Image Processing ER - TY - CHAP TI - Classification of Severity of COVID-19 Patients Based on the Heart Rate Variability AU - Pordeus, Daniel AU - Ribeiro, Pedro AU - Zacarias, Laíla AU - Paulo Madeiro, João AU - Lobo Marques, Joao Alexandre AU - Miguel Rodrigues, Pedro AU - Leite, Camila AU - Alves Neto, Manoel AU - Aires Peixoto Jr, Arnaldo AU - de Oliveira, Adriel T2 - Computerized Systems for Diagnosis and Treatment of COVID-19 A2 - Lobo Marques, Joao Alexandre A2 - Fong, Simon James AB - The continuous development of robust machine learning algorithms in recent years has helped to improve the solutions of many studies in many fields of medicine, rapid diagnosis and detection of high-risk patients with poor prognosis as the coronavirus disease 2019 (COVID-19) spreads globally, and also early prevention of patients and optimization of medical resources. Here, we propose a fully automated machine learning system to classify the severity of COVID-19 from electrocardiogram (ECG) signals. We retrospectively collected 100 5-minute ECGs from 50 patients in two different positions, upright and supine. We processed the surface ECG to obtain QRS complexes and HRV indices for RR series, including a total of 43 features. We compared 19 machine learning classification algorithms that yielded different approaches explained in a methodology session. CY - Cham DA - 2023/// PY - 2023 DP - Springer Link SP - 155 EP - 177 LA - en PB - Springer International Publishing SN - 978-3-031-30788-1 UR - https://doi.org/10.1007/978-3-031-30788-1_10 Y2 - 2023/10/10/04:37:17 KW - COVID-19 KW - Electrocardiogram (ECG) signal KW - Heart Rate Variability (HRV) indices KW - Severity KW - Signal processing ER - TY - CHAP TI - Classification of COVID-19 CT Scans Using Convolutional Neural Networks and Transformers AU - Bernardo Gois, Francisco Nauber AU - Lobo Marques, Joao Alexandre AU - Fong, Simon James T2 - Computerized Systems for Diagnosis and Treatment of COVID-19 A2 - Lobo Marques, Joao Alexandre A2 - Fong, Simon James AB - COVID-19 is a respiratory disorder caused by CoronaVirus and SARS (SARS-CoV2). WHO declared COVID-19 a global pandemic in March 2020 and several nations’ healthcare systems were on the verge of collapsing. With that, became crucial to screen COVID-19-positive patients to maximize limited resources. NAATs and antigen tests are utilized to diagnose COVID-19 infections. NAATs reliably detect SARS-CoV-2 and seldom produce false-negative results. Because of its specificity and sensitivity, RT-PCR can be considered the gold standard for COVID-19 diagnosis. This test’s complex gear is pricey and time-consuming, using skilled specialists to collect throat or nasal mucus samples. These tests require laboratory facilities and a machine for detection and analysis. Deep learning networks have been used for feature extraction and classification of Chest CT-Scan images and as an innovative detection approach in clinical practice. Because of COVID-19 CT scans’ medical characteristics, the lesions are widely spread and display a range of local aspects. Using deep learning to diagnose directly is difficult. In COVID-19, a Transformer and Convolutional Neural Network module are presented to extract local and global information from CT images. This chapter explains transfer learning, considering VGG-16 network, in CT examinations and compares convolutional networks with Vision Transformers (ViT). Vit usage increased VGG-16 network F1-score to 0.94. CY - Cham DA - 2023/// PY - 2023 DP - Springer Link SP - 79 EP - 97 LA - en PB - Springer International Publishing SN - 978-3-031-30788-1 UR - https://doi.org/10.1007/978-3-031-30788-1_6 Y2 - 2023/10/10/04:37:10 ER - TY - CHAP TI - Artificial neural network-based approaches for computer-aided disease diagnosis and treatment AU - Marques, João Alexandre Lôbo AU - Gois, Francisco Nauber Bernardo AU - Madeiro, João Paulo do Vale AU - Li, Tengyue AU - Fong, Simon James T2 - Cognitive and Soft Computing Techniques for the Analysis of Healthcare Data A2 - Bhoi, Akash Kumar A2 - de Albuquerque, Victor Hugo C. A2 - Srinivasu, Parvathaneni Naga A2 - Marques, Gonçalo T3 - Intelligent Data-Centric Systems AB - 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. DA - 2022/01/01/ PY - 2022 DP - ScienceDirect SP - 79 EP - 99 LA - en PB - Academic Press SN - 978-0-323-85751-2 UR - https://www.sciencedirect.com/science/article/pii/B9780323857512000086 Y2 - 2022/09/21/02:36:30 KW - Artificial intelligence KW - Computer-aided diagnosis and treatment KW - Deep learning KW - Medical imaging KW - Neural networks ER - TY - CHAP TI - Artificial Intelligence Prediction for the COVID-19 Data Based on LSTM Neural Networks and H2O AutoML AU - Marques, Joao Alexandre Lobo AU - Gois, Francisco Nauber Bernardo AU - Xavier-Neto, José AU - Fong, Simon James T2 - Predictive Models for Decision Support in the COVID-19 Crisis T3 - Predictive Models for Decision Support in the COVID-19 Crisis DA - 2020/12// PY - 2020 DP - ORCID SP - 69 EP - 87 PB - Springer International Publishing UR - https://doi.org/10.1007%2F978-3-030-61913-8_5 Y2 - 2021/02/03/09:10:21 ER - TY - CHAP TI - Analysis of the COVID19 Pandemic Behaviour Based on the Compartmental SEAIRD and Adaptive SVEAIRD Epidemiologic Models AU - Fong, Simon James AU - Marques, João Alexandre Lobo AU - Li, G. AU - Dey, N. AU - Crespo, Rubén G. AU - Herrera-Viedma, E. AU - Gois, F. Nauber Bernardo AU - Neto, José Xavier T2 - Epidemic Analytics for Decision Supports in COVID19 Crisis A2 - Marques, Joao Alexandre Lobo A2 - Fong, Simon James AB - 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. CY - Cham DA - 2022/// PY - 2022 DP - Springer Link SP - 17 EP - 64 LA - en PB - Springer International Publishing SN - 978-3-030-95281-5 UR - https://doi.org/10.1007/978-3-030-95281-5_2 Y2 - 2022/09/21/02:33:52 KW - Adaptive SEAIRD model KW - Adaptive SVEAIRD model KW - Asymptomatic cases KW - Prediction models ER - TY - CHAP TI - AI and deep learning for processing the huge amount of patient-centric data that assist in clinical decisions AU - Lôbo Marques, João Alexandre AU - Bernardo Gois, Francisco Nauber AU - Nunes da Silveira, Jarbas Aryel AU - Li, Tengyue AU - Fong, Simon James T2 - Cognitive and Soft Computing Techniques for the Analysis of Healthcare Data A2 - Bhoi, Akash Kumar A2 - de Albuquerque, Victor Hugo C. A2 - Srinivasu, Parvathaneni Naga A2 - Marques, Gonçalo T3 - Intelligent Data-Centric Systems AB - 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. DA - 2022/01/01/ PY - 2022 DP - ScienceDirect SP - 101 EP - 121 LA - en PB - Academic Press SN - 978-0-323-85751-2 UR - https://www.sciencedirect.com/science/article/pii/B9780323857512000013 Y2 - 2022/09/21/04:34:03 KW - Artificial intelligence KW - Clinical decisions KW - Computer-aided diagnostic systems KW - Deep learning KW - Patient-centric data ER - TY - JOUR TI - A review on multimodal machine learning in medical diagnostics AU - Yan, Keyue AU - Li, Tengyue AU - Marques, João Alexandre Lobo AU - Gao, Juntao AU - Fong, Simon James AU - Yan, Keyue AU - Li, Tengyue AU - Marques, João Alexandre Lobo AU - Gao, Juntao AU - Fong, Simon James T2 - Mathematical Biosciences and Engineering AB - Nowadays, the increasing number of medical diagnostic data and clinical data provide more complementary references for doctors to make diagnosis to patients. For example, with medical data, such as electrocardiography (ECG), machine learning algorithms can be used to identify and diagnose heart disease to reduce the workload of doctors. However, ECG data is always exposed to various kinds of noise and interference in reality, and medical diagnostics only based on one-dimensional ECG data is not trustable enough. By extracting new features from other types of medical data, we can implement enhanced recognition methods, called multimodal learning. Multimodal learning helps models to process data from a range of different sources, eliminate the requirement for training each single learning modality, and improve the robustness of models with the diversity of data. Growing number of articles in recent years have been devoted to investigating how to extract data from different sources and build accurate multimodal machine learning models, or deep learning models for medical diagnostics. This paper reviews and summarizes several recent papers that dealing with multimodal machine learning in disease detection, and identify topics for future research. DA - 2023/// PY - 2023 DO - 10.3934/mbe.2023382 DP - www.aimspress.com VL - 20 IS - 5 SP - 8708 EP - 8726 J2 - MBE LA - en SN - 1551-0018 UR - http://www.aimspress.com/rticle/doi/10.3934/mbe.2023382 Y2 - 2023/03/21/16:22:08 ER - TY - CHAP TI - A Quantum Field Formulation for a Pandemic Propagation AU - Arraut, Ivan AU - Marques, João Alexandre Lobo AU - Fong, Simon James AU - Li, Gloria AU - Gois, Francisco Nauber Bernardo AU - Neto, José Xavier T2 - Epidemic Analytics for Decision Supports in COVID19 Crisis A2 - Marques, Joao Alexandre Lobo A2 - Fong, Simon James AB - In this chapter, a mathematical model explaining generically the propagation of a pandemic is proposed, helping in this way to identify the fundamental parameters related to the outbreak in general. Three free parameters for the pandemic are identified, which can be finally reduced to only two independent parameters. The model is inspired in the concept of spontaneous symmetry breaking, used normally in quantum field theory, and it provides the possibility of analyzing the complex data of the pandemic in a compact way. Data from 12 different countries are considered and the results presented. The application of nonlinear quantum physics equations to model epidemiologic time series is an innovative and promising approach. CY - Cham DA - 2022/// PY - 2022 DP - Springer Link SP - 141 EP - 158 LA - en PB - Springer International Publishing SN - 978-3-030-95281-5 UR - https://doi.org/10.1007/978-3-030-95281-5_6 Y2 - 2022/09/21/02:32:32 KW - COVID-19 KW - Mathematical modeling KW - Nonlinear analysis KW - Quantum field theory ER -