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...

Descripción completa

Detalles Bibliográficos
Clasificación:Libro Electrónico
Otros Autores: Hameed, Kauser (Editor ), Bhatia, Surbhi, 1988- (Editor ), Ahmed, Syed Tousif
Formato: Electrónico eBook
Idioma:Inglés
Publicado: San Diego : Elsevier Science & Technology, 2021.
Temas:
Disease management > Data processing.
Epidemiology > Statistical methods.
COVID-19 (Disease)
Artificial intelligence > Medical applications.
COVID-19
Gestion th�erapeutique > Informatique.
�Epid�emiologie > M�ethodes statistiques.
COVID-19.
Intelligence artificielle en m�edecine.
Artificial intelligence > Medical applications
Epidemiology > Statistical methods
Acceso en línea:Texto completo
Tabla de Contenidos:
  • 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.
  • 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.
  • 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.

Ejemplares similares