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 - 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 - 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 - 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 - 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 - 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 - 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 - Malaria Blood Smears Object Detection Based on Convolutional DCGAN and CNN Deep Learning Architectures AU - Gois, Francisco Nauber Bernardo AU - Marques, João Alexandre Lobo AU - de Oliveira Dantas, Allberson Bruno AU - Santos, Márcio Costa AU - Neto, José Valdir Santiago AU - de Macêdo, José Antônio Fernandes AU - Du, Wencai AU - Li, Ye T2 - Computer and Information Science A2 - Lee, Roger T3 - Studies in Computational Intelligence AB - Fast and efficient malaria diagnostics are essential in efforts to detect and treat the disease in a proper time. The standard approach to diagnose malaria is a microscope exam, which is submitted to a subjective interpretation. Thus, the automating of the diagnosis process with the use of an intelligent system capable of recognizing malaria parasites could aid in the early treatment of the disease. Usually, laboratories capture a minimum set of images in low quality using a system of microscopes based on mobile devices. Due to the poor quality of such data, conventional algorithms do not process those images properly. This paper presents the application of deep learning techniques to improve the accuracy of malaria plasmodium detection in the presented context. In order to increase the number of training sets, deep convolutional generative adversarial networks (DCGAN) were used to generate reliable training data that were introduced in our deep learning model to improve accuracy. A total of 6 experiments were performed and a synthesized dataset of 2.200 images was generated by the DCGAN for the training phase. For a real image database with 600 blood smears with malaria plasmodium, the proposed Deep Learning architecture obtained the accuracy of 100% for the plasmodium detection. The results are promising and the solution could be employed to support a mass medical diagnosis system. CY - Cham DA - 2023/// PY - 2023 DP - Springer Link SP - 197 EP - 212 LA - en PB - Springer International Publishing SN - 978-3-031-12127-2 UR - https://doi.org/10.1007/978-3-031-12127-2_14 Y2 - 2023/03/22/06:27:01 ER - TY - CHAP TI - Malaria Blood Smears Object Detection Based on Convolutional DCGAN and CNN Deep Learning Architectures AU - Gois, Francisco Nauber Bernardo AU - Marques, João Alexandre Lobo AU - De Oliveira Dantas, Allberson Bruno AU - Santos, Márcio Costa AU - Neto, José Valdir Santiago AU - De Macêdo, José Antônio Fernandes AU - Du, Wencai AU - Li, Ye T2 - Computer and Information Science A2 - Lee, Roger CY - Cham DA - 2023/// PY - 2023 DP - DOI.org (Crossref) VL - 1055 SP - 197 EP - 212 LA - en PB - Springer International Publishing SN - 9783031121265 9783031121272 UR - https://link.springer.com/10.1007/978-3-031-12127-2_14 Y2 - 2023/04/26/07:34:23 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 - 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 -