Researches and Applications of Artificial Intelligence to Mitigate Pandemics History, Diagnostic Tools, Epidemiology, Healthcare, and Technology
Researches and Applications of Artificial Intelligence to Mitigate Pandemics: History, Diagnostic Tools, Epidemiology, Healthcare, and Technology offers readers an interdisciplinary view of state-of-art research related to the COVID-19 outbreak, with a focus on tactics employed to model the number o...
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| Main Authors | , , |
|---|---|
| Format | eBook |
| Language | English |
| Published |
Chantilly
Elsevier Science & Technology
2021
Academic Press |
| Edition | 1 |
| Series | Hybrid Computational Intelligence for Pattern Analysis and Understanding |
| Subjects | |
| Online Access | Get full text |
| ISBN | 9780323909594 0323909590 |
| DOI | 10.1016/C2020-0-03006-9 |
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| Abstract | Researches and Applications of Artificial Intelligence to Mitigate Pandemics: History, Diagnostic Tools, Epidemiology, Healthcare, and Technology offers readers an interdisciplinary view of state-of-art research related to the COVID-19 outbreak, with a focus on tactics employed to model the number of cases of COVID-19 (time series modeling), models employed to diagnostics COVID-19 based on images, and the panoramic of COVID-19 since its discovery and up to this book's publication. This book showcases the algorithms and models available to manage pandemic data, the role of AI, IoT and Mathematical Modeling, how to prevent and fight COVID-19, and the existing medical, social and pharmaceutical support. Chapters cover methods and protocols, the basics and history of diseases, the fast diagnosis of disease with different automated algorithms and artificial intelligence tools and techniques, the methods of handling epidemiology for mitigating the spread of disease, artificial intelligence and mathematical modeling techniques, and how mental and physical health is affected with social media usage. |
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| AbstractList | Researches and Applications of Artificial Intelligence to Mitigate Pandemics: History, Diagnostic Tools, Epidemiology, Healthcare, and Technology offers readers an interdisciplinary view of state-of-art research related to the COVID-19 outbreak, with a focus on tactics employed to model the number of cases of COVID-19 (time series modeling), models employed to diagnostics COVID-19 based on images, and the panoramic of COVID-19 since its discovery and up to this book's publication. This book showcases the algorithms and models available to manage pandemic data, the role of AI, IoT and Mathematical Modeling, how to prevent and fight COVID-19, and the existing medical, social and pharmaceutical support. Chapters cover methods and protocols, the basics and history of diseases, the fast diagnosis of disease with different automated algorithms and artificial intelligence tools and techniques, the methods of handling epidemiology for mitigating the spread of disease, artificial intelligence and mathematical modeling techniques, and how mental and physical health is affected with social media usage. |
| Author | Khan, Surbhi Bhatia Ahmed, Syed Tousi Hameed, Kauser |
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| Snippet | Researches and Applications of Artificial Intelligence to Mitigate Pandemics: History, Diagnostic Tools, Epidemiology, Healthcare, and Technology offers... |
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| SubjectTerms | Artificial intelligence COVID-19 (Disease) Disease management Epidemiology |
| Subtitle | History, Diagnostic Tools, Epidemiology, Healthcare, and Technology |
| TableOfContents | Cover -- Title Page -- Copyright -- Contents -- Contributors -- Chapter 1 - A case of 2019-nCoV novel coronavirus outbreak -- 1.1 - Introduction -- 1.1.1 - History of coronavirus -- 1.1.2 - Novel coronavirus-2019 -- 1.1.3 - Infectivity of COVID-19 -- 1.1.4 - Clinical symptoms and its effect -- 1.2 - Necessary precautions -- 1.2.1 - Appropriate mask and its availability -- 1.2.2 - Role of disinfectants -- 1.2.3 Immunity boosters -- 1.3 - Demystify COVID-19 -- 1.3.1 - Suspicious symptoms -- 1.3.2 - Available approaches for treatment -- 1.3.3 - Medical observation -- 1.3.4 - Reinfection -- 1.4 - Dispelling rumors -- 1.4.1 - Young people and COVID-19 -- 1.4.2 - Medicines available for curing virus -- 1.5 - Conclusion -- References -- Chapter 2 - Diagnostic tools and automated decision support systems for COVID-19 -- 2.1 - Introduction -- 2.2 - Molecular assay-based diagnosis -- 2.2.1 - Reverse transcriptase polymerase chain reaction -- 2.2.2 - RT-PCR assay procedure -- 2.2.3 - Diagnostic precision of RT-PCR-based diagnosis -- 2.2.4 - Limitations of RT-PCR-based diagnosis -- 2.2.5 - Conclusion -- 2.3 - Serological and immunological assay-based diagnosis -- 2.3.1 - Types of serology-based testing -- 2.3.2 - Diagnostic precision of serology-based testing -- 2.3.3 Uses of laboratory-based assays in the context of AI and data science -- 2.3.4 Conclusion -- 2.4 - Chest and lung imaging-based diagnosis -- 2.4.1 - Chest X-ray imaging modality -- 2.4.2 - COVID-19 diagnosis using chest X-ray -- 2.4.3 - Computer-aided diagnosis (CAD) using chest X-ray -- 2.4.3.1 - COVID-19 chest X-ray datasets -- 2.4.3.2 - Machine learning-driven image-based diagnosis -- 2.4.3.3 - Advanced machine and deep learning techniques -- 2.4.4 - Diagnostic precision of CXR-based diagnosis -- 2.4.5 - Benefits and limitations of CXR-based diagnosis 3.11 - SIR model -- 3.12 - Summary -- References -- Chapter 4 - Social media sentiment analysis and emotional intelligence including women role during COVID-19 crisis -- 4.1 - Introduction -- 4.2 - Background -- 4.2.1 - Importance of social media -- 4.2.2 - Social media versus misleading information -- 4.2.2.1 - Misleading information on social media -- 4.2.2.2 - Infodemic -- 4.2.2.3 - Causes for COVID-19 pandemic to become alarming -- 4.2.2.4 - Instead of wasting time on irrelevant websites and social media platforms on pandemic, we can trust the following... -- 4.2.2.5 - Steps taken by WHO to handle with infodemic during the COVID-19 pandemic -- 4.2.3 - Sentiment analysis and emotional intelligence -- 4.2.3.1 - Sentiment analysis -- 4.2.3.1.1 - There are two types of sentiment analysis -- 4.2.3.1.2 - Advantages of social media sentiments during COVID-19 -- 4.2.3.1.3 - Disadvantages of sentiment analysis -- 4.2.3.2 - Emotional intelligence -- 4.2.3.2.1 - Attributes of emotional intelligence -- 4.2.3.2.2 - Advantages of emotional intelligence -- 4.2.3.2.3 - Enhancing our emotional intelligence as follows -- 4.2.3.2.4 - How to lessen pressure in pandemic crisis -- 4.2.3.2.5 - Some of the responses of the society during this COVID-19 are as follows -- 4.3 - Role of women during COVID-19 pandemic -- 4.3.1 - Women care, duties, and COVID-19 -- 4.3.1.1 - Women care and exterior duties -- 4.3.1.2 - Influence of COVID-19 on the health and femininity -- 4.3.1.3 - Internal duties of women at home -- 4.3.2 - Women duties during COVID-19 -- 4.3.2.1 - Female employees and job risk -- 4.3.2.2 - Female poverty -- 4.3.2.3 - Role of females in LED countries during COVID-19 -- 4.3.2.4 - Women home confinement and gender inequality during COVID-19 -- 4.3.3 - Principle measures and principle alternatives -- 4.3.3.1 - Assist women, workers, and families 4.3.3.2 - Assisting women and families facing jobless -- 4.3.3.3 - Helping sufferers of gender-based violence and providing equality -- 4.3.3.4 - Gender assessment, influence, and budgeting -- 4.3.3.5 - Alternatives for protecting the betterment of the gender-related sustainable development goals -- 4.4 - Related works -- 4.5 - Result and discussion -- 4.6 - Conclusions -- References -- Chapter 5 - Role of technology in COVID-19 pandemic -- 5.1 - Introduction -- 5.2 - Technology and medical science -- 5.2.1 - Electrocardiography (EKG) -- 5.2.2 - X-ray -- 5.2.3 - Ultrasound -- 5.2.4 - MRI -- 5.3 - Past pandemics and technology -- 5.3.1 - Simulation models -- 5.3.2 - Electronic surveillance system -- 5.3.3 - Monitoring online search engines -- 5.4 - Use of technology during COVID-19 -- 5.4.1 - Internet of Things (IoT) and Internet of Medical Things (IoMT) -- 5.4.1.1 - IoMT device classification -- 5.4.1.1.1 - Wearables -- 5.4.1.1.2 Remote patient monitoring devices -- 5.4.1.1.3 Point-of-care devices -- 5.4.1.2 Internet of Medical Things in COVID-19 -- 5.4.1.2.1 - Disease diagnosis -- 5.4.1.2.2 - Disease monitoring -- 5.4.1.2.3 - Disease management -- 5.4.2 - Drone technology -- 5.4.2.1 Versatility in drones -- 5.4.2.2 Usability of drones during COVID-19 pandemic -- 5.4.2.2.1 Drones as telemedicine and transfer units -- 5.4.2.2.2 Drones for surveillance and screening -- 5.4.2.2.3 Drones for public announcements -- 5.4.2.2.4 - Drones for disinfecting places -- 5.4.3 Robots -- 5.4.3.1 Usability in COVID-19 pandemic -- 5.4.3.2 - Robots replacing humans -- 5.4.3.3 Unmanned vehicles -- 5.4.4 Bluetooth and GPS technology -- 5.4.4.1 - Applications of GPS -- 5.4.4.2 - Asymptomatic and suspected patients tracking -- 5.4.4.2.1 - Contact tracing -- 5.4.5 - Telemedicine: a new era -- 5.5 Case study -- 5.5.1 AAROGYA SETU app -- 5.6 Conclusion -- References Chapter 6 - Conclusions -- 6.1 - Introduction -- 6.2 - Data science and its applications -- 6.2.1 - Patient prioritization to control risk -- 6.2.2 - Diagnosis and screening -- 6.2.3 - Modeling for epidemic -- 6.2.4 - Tracing the contacted people -- 6.2.5 - Acknowledging social interventions -- 6.2.6 - Use of case data -- 6.2.7 - Textual data -- 6.2.8 - Biomedical data -- 6.2.9 - Other supportive datasets -- 6.2.10 - Competition database -- 6.3 - Survey on ongoing research -- 6.3.1 - Image data analysis -- 6.3.2 - Audio data analysis -- 6.3.3 - Sensor data analysis -- 6.3.4 - Drug discovery analysis -- 6.4 - Bibliometric data collection -- 6.5 - Data science and cross cutting challenges -- 6.5.1 - Data confines -- 6.5.2 - Exactitude of output versus urgency -- 6.5.3 - Ethics, security, and privacy -- 6.5.4 - Requirement of multidisciplinary collaboration -- 6.5.5 - Latest data modalities -- 6.5.6 - Results for the developing world -- 6.6 - Summary -- References -- Index -- Back cover 2.4.6 - Chest CT-scan imaging modality -- 2.4.6.1 - CT-scan features -- 2.4.7 COVID-19 diagnosis using chest CT scan -- 2.4.8 Computer-aided diagnosis using chest CT scan -- 2.4.8.1 - COVID-19 CT datasets -- 2.4.8.2 - Deep learning techniques for CT-based diagnosis -- 2.4.9 - Diagnostic precision of CT-based diagnosis -- 2.4.10 - Benefits and limitations of CT-based diagnosis -- 2.4.11 - Case study: radiology observations vs. CAD -- 2.4.12 - Lung ultrasound imaging modality -- 2.4.13 - COVID-19 diagnosis using lung ultrasound -- 2.4.13.1 - Computer-aided diagnosis using lung ultrasound -- 2.4.13.2 - Diagnostic precision of ultrasound-based diagnosis -- 2.4.13.3 - Benefits and limitations of ultrasound-based diagnosis -- 2.4.14 - Conclusion -- References -- Chapter 3 - Epidemiology -- 3.1 - Introduction -- 3.2 - The mathematical modeling establishment in epidemiology -- 3.3 - Mathematical modeling methodologies in epidemiology -- 3.4 - The philosophy of mathematical modeling -- 3.4.1 - Complexity of the model -- 3.4.1.1 - Determined complexity of a model -- 3.4.2 - Testing of hypothesis and formulation of a model -- 3.5 - The nature of epidemiological data -- 3.5.1 - Stationary time series -- 3.5.2 - Autocorrelograms -- 3.6 - Microparasitic infections from childhood -- 3.7 - A simple epidemic model - COVID case studies -- 3.7.1 - Different models -- 3.7.2 - The process of transmission -- 3.7.3 - Between-compartment flux of individuals -- 3.7.3.1 - Modeling the infectious period -- 3.7.4 - Dynamics analysis and deterministic setup of COVID -- 3.7.5 - The average age and statistics at infection -- 3.7.6 - Data analysis vs COVID cases -- 3.7.6.1 - Parameter estimations -- 3.7.6.2 - Recovery rate -- 3.7.6.3 - Monte Carlo simulation -- 3.8 - SEIQDR model -- 3.8.1 - SEIR model -- 3.8.2 - SEIQDR model -- 3.9 - SEQIR model -- 3.10 - SEIARD model |
| Title | Researches and Applications of Artificial Intelligence to Mitigate Pandemics |
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