Application of artificial intelligence in COVID-19

Application of artificial intelligence in COVID-19 / Sachi Nandan Mohanty, Shailendra K. Saxena, Suneeta Satpathy, Jyotir Moy Chatterjee, editors.

The book examines the role of artificial intelligence during the COVID-19 pandemic, including its application in i) early warnings and alerts, ii) tracking and prediction, iii) data dashboards, iv) diagnosis and prognosis, v) treatments, and cures, and vi) social control. It explores the use of arti...

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Bibliographic Details
Other Authors: Mohanty, Sachi Nandan (Editor), Saxena, Shailendra K., 1950- (Editor), Satpathy, Suneeta (Editor), Chatterjee, Jyotir Moy (Editor)
Format: eBook
Language:English
Published: Singapore : Springer, [2021]
Series:Medical virology (Springer (Firm))
Subjects:
Artificial intelligence > Medical applications.
COVID-19 (Disease) > Data processing.
Artificial intelligence.
artificial intelligence.
Artificial intelligence > Medical applications
COVID-19 (Disease) > Data processing
COVID-19.
Intel·ligència artificial en medicina.
Llibres electrònics.
Online Access:Click for online access
Table of Contents:
  • Intro
  • Foreword 1
  • Foreword 2
  • Preface
  • Acknowledgements
  • Contents
  • About the Editors
  • Part I: AI as a Source of Prides for Healthcare
  • 1: Comprehensive Claims of AI for Healthcare Applications-Coherence Towards COVID-19
  • 1.1 Orientation of Artificial Intelligence in Healthcare Research
  • 1.2 Correlated Investigational Analysis of AI Appliances in Healthcare System and Various Clinical Diseases
  • 1.2.1 Disease Detection and Diagnosis
  • 1.2.2 Automated Robert Treatment and Drug Design, Discovery
  • 1.2.3 Healthcare Data Management Supported by Digital Managerial Application
  • 1.2.4 AI in Public and Clinical Health
  • 1.3 Motivational AI Devices for Healthcare
  • 1.3.1 AI-Administered Devices with Machine Learning and Deep Learning
  • 1.3.2 AI Attributed Devices with IOT
  • 1.3.3 AI Supervised Devices with Big Data and Data Science
  • 1.3.4 AI-Based Mining and NLP
  • 1.3.5 AI-Enabled Expert System
  • 1.4 Demand of AI for COVID-19
  • 1.4.1 Prior Alert Generation
  • 1.4.2 Continuous Tracing and Following COVID-19 Symptoms
  • 1.4.3 Diagnosis and Prognosis
  • 1.4.4 Treatment and Possible Drug Design and Discovery
  • 1.4.5 Control over Society and People with Guidelines
  • 1.5 Conclusions and Future Work
  • 1.6 Executive Summary
  • References
  • 2: Artificial Intelligence-Based Systems for Combating COVID-19
  • 2.1 Introduction
  • 2.2 How Technology Can Help in Containing the Pandemic?
  • 2.3 Technological Approach Vs Non-technological Approach of Treatment of COVID-19
  • 2.4 Existing Technologies to Detect/Diagnose the Virus
  • 2.4.1 Non-contact Infrared Thermometers
  • 2.5 Thermal Screening via Thermal Cameras
  • 2.5.1 Symptom-Based Diagnosis
  • 2.5.2 Ventilators
  • 2.6 Means of Prevention from COVID-19
  • 2.6.1 Masks
  • 2.6.2 Sanitizers/Hand Rub
  • 2.6.3 Sanitizing Tunnels for Public Areas.
  • 2.6.4 Washing Hands with Soap for 20s
  • 2.6.5 Avoiding Handshakes
  • 2.7 Use of Modern Technologies for Making Diagnosis Faster, Easier, and Effective
  • 2.8 Proposed Techniques to Effectively Control the Rise in Cases of COVID-19
  • 2.8.1 Crowdsource-Based Applications
  • 2.9 Conclusion
  • References
  • Part II: AI Warfare in COVID-19 Diagnosis, Detection, Prediction, Prognosis and Knowledge Representation
  • 3: Artificial Intelligence-Mediated Medical Diagnosis of COVID-19
  • 3.1 Introduction
  • 3.2 Pathogenesis and Diagnostic Windows
  • 3.3 AI Assisted COVID-19 Diagnosis
  • 3.3.1 Potential Application for Infection Detection
  • 3.3.2 Application of AI on `Omics´ Big-Data
  • 3.3.3 Use of AI on Radiology Data
  • 3.4 Future Directions
  • References
  • 4: Artificial Intelligence (AI) Combined with Medical Imaging Enables Rapid Diagnosis for Covid-19
  • 4.1 Introduction
  • 4.1.1 Reverse Transcription-Polymerase Chain Reaction
  • 4.1.2 Isothermal Amplification Assays
  • 4.1.3 Antigen Tests
  • 4.1.4 Serological Tests
  • 4.1.5 Rapid Diagnostic Tests (RDT)
  • 4.1.6 Enzyme-Linked ImmunoSorbent Assay (ELISA)
  • 4.1.7 Neutralization Assay
  • 4.1.8 Chemiluminescent Immunoassay
  • 4.2 AI-Based Diagnosis
  • 4.2.1 Chest CT or X-ray CT Scans
  • 4.2.2 Chest Radiography
  • 4.2.2.1 Limitation
  • 4.3 Other Predictive Measures for Covid-19 Diagnosis
  • 4.3.1 Pulse Oximetry
  • 4.3.2 Thermal Screening
  • 4.4 Conclusions
  • References
  • 5: Role of Artificial Intelligence in COVID-19 Prediction Based on Statistical Methods
  • 5.1 Introduction
  • 5.2 Related Work
  • 5.3 Dataset Description
  • 5.4 Experimental Results
  • 5.4.1 Combinatorial (Quick) Approach
  • 5.4.2 Stepwise Forward Selection Approach
  • 5.4.3 Stepwise Mixed Selection Approach
  • 5.4.4 GMDH Neural Network Approach
  • 5.5 Comparison Between the Algorithms Based on MAE, RMSE, SD, Correlation.
  • 5.6 Conclusion
  • References
  • 6: Data-Driven Symptom Analysis and Location Prediction Model for Clinical Health Data Processing and Knowledgebase Developmen...
  • 6.1 Introduction
  • 6.2 Related Work
  • 6.3 Rudiments of Random Forest Machine Learning Algorithm
  • 6.4 Case Study for Symptom Analysis and Its Prediction with Random Forest Using COVID-19 WHO Data Set
  • 6.4.1 Step Wise Experimental Result Analysis and Discussions
  • 6.4.2 Calculation of Average Baseline Error
  • 6.4.3 Classifying Into Zones
  • 6.4.3.1 Setting Threshold Value
  • 6.4.4 Color Attribute of Map with Zones (Green, Orange, and Red)
  • 6.5 Augmented Enhancements to the Detection and Prediction Analysis for COVID 19
  • 6.5.1 Appending a New Drop-Down Menu in the Detection Page
  • 6.6 Aligning Output of This Research as a Supplement to Heighten Up Healthcare and Public Health
  • 6.7 Conclusions
  • 6.8 Future Work
  • References
  • 7: A Decision Support System Using Rule-Based Expert System for COVID-19 Prediction and Diagnosis
  • 7.1 Introduction
  • 7.2 Background
  • 7.2.1 Machine Learning-Based Data-Oriented Approach
  • 7.2.2 Expert System-Based Knowledge-Oriented Approach
  • 7.3 Overview of Expert System
  • 7.3.1 Fundamentals
  • 7.3.2 Expert System Architecture
  • 7.3.3 Expert System Design Issues
  • 7.4 Case Study: COVID-19
  • 7.4.1 Feasibility of Expert System on COVID-19
  • 7.4.2 Problem Description
  • 7.4.3 Proposed Expert System: ESCOVID
  • 7.4.3.1 Rule Set and Knowledgebase
  • 7.4.3.2 Inference Mechanism
  • 7.5 Implementation and Testing
  • 7.6 Conclusion
  • References
  • 8: A Predictive Mechanism to Intimate the Danger of Infection via nCOVID-19 Through Unsupervised Learning
  • 8.1 Introduction
  • 8.2 Literature Survey
  • 8.3 Methodology
  • 8.3.1 Data Collection
  • 8.3.2 Relevant Dataset
  • 8.3.3 Data Processing
  • 8.3.3.1 Algorithm of Clustering
  • 8.4 Result Analysis.
  • 8.4.1 Overall Behavior of All Unsupervised Learning Model (Figs. 8.9 and 8.10)
  • 8.5 Conclusion
  • References
  • 9: Artificial Intelligence-Enabled Prognosis Technologies for SARS-CoV-2/COVID-19
  • 9.1 Introduction
  • 9.1.1 Epidemiology and Phylogeography of Pathogen
  • 9.1.2 Human-to-Human Transmission
  • 9.1.3 Clinical Phenotype Variations and Pathogenesis
  • 9.2 Current Prognosis Practices
  • 9.2.1 Diagnosis Services
  • 9.2.2 Control Practices
  • 9.2.2.1 Sanitization
  • 9.2.2.2 Treatment
  • 9.3 Challenges of SARS-CoV-2
  • 9.3.1 Phylogeography and Clinical Features
  • 9.3.2 Mass Community and Healthcare Management
  • 9.3.3 Transmission and Distancing
  • 9.3.4 Diagnosis and Treatment
  • 9.3.5 Disease Modeling Approaches
  • 9.3.6 Data Security Concerns
  • 9.4 Advanced Technologies
  • 9.4.1 Internet of Things (IoT)
  • 9.4.2 Artificial Intelligence (AI)
  • 9.4.3 Databases and Analytics
  • 9.4.4 Advanced Genomics and proteomics
  • 9.4.5 Cloud Computing and Optimization
  • 9.4.6 Digital Medicine and Healthcare
  • 9.4.7 Biosensor and Bioelectronics
  • 9.5 Integrated Technology and Logical Products
  • 9.5.1 AI, Cloud, Sensor and IoT
  • 9.6 AI-Enabled Prognosis Technology, Product, and Model Description
  • 9.6.1 Technology and Product: AI Analysis and Program in Healthcare
  • 9.6.2 Product and Technology: AI-Based sanitization Machine Using Cloud computing and Optimization
  • 9.6.3 Product and Technology: IOT-Based AI-Enabled Touchless Hand Sanitizer Machine
  • 9.6.4 Technology and Model: Prognosis Healthcare Model for Mass Community
  • 9.6.4.1 Standard Prognosis Practices
  • 9.7 Adaptation of AI-Enabled Technology and Disease Research
  • 9.7.1 Hygiene, Distancing, and Virus Control
  • 9.7.2 Understanding of Pathogenic Consequences
  • 9.8 Conclusion
  • 9.9 Future Prospects
  • References.
  • 10: Intelligent Agent Based Case Base Reasoning Systems Build Knowledge Representation in COVID-19 Analysis of Recovery of Inf...
  • 10.1 Introduction
  • 10.2 Related Work
  • 10.3 COVID-19
  • 10.4 Symptom of COVID-19
  • 10.5 Artificial Intelligence
  • 10.6 Machine Learning
  • 10.7 Natural Language Processing
  • 10.8 Robotics
  • 10.9 Autonomous Vehicles
  • 10.10 Vision
  • 10.11 Clinical Artificial Intelligence
  • 10.12 Expert System
  • 10.13 Machine Learning
  • 10.14 Intelligent Agent
  • 10.15 Characteristic Agents
  • 10.16 Clinical Intelligent Agent
  • 10.17 Multi-Agent System
  • 10.18 Java Agent Framework (JADE)
  • 10.19 Clinical Multi-Agents
  • 10.20 Case Base Reasoning
  • 10.21 The CBR Cycle
  • 10.22 JCOLIBRI
  • 10.23 Clinical Case Base Reasoning Systems
  • 10.24 Knowledge Base System
  • 10.25 Clinical Knowledge Base System
  • 10.26 Amalgamation OF CAI, CIA, CMAS, CCBR Using in KBSCOVID-19 Model
  • 10.27 Implementation of MASCBR-Based Knowledge Base Patients Recovery from COVID-19 Pandemic
  • 10.28 Conclusion
  • 10.29 Future Work
  • References
  • Part III: Machine Learning Solicitation for COVID 19
  • 11: Epidemic Analysis of COVID-19 Using Machine Learning Techniques
  • 11.1 Introduction
  • 11.2 Related Work
  • 11.3 Pattern Identification for COVID-19
  • 11.4 Experiment Analysis
  • 11.4.1 Dataset 1: Based on Geographic Distribution (https://www.ecdc.europa.eu/en/publications-data/download-todays-data-geogr...
  • 11.4.1.1 Description of the Dataset
  • 11.4.1.2 Correlation Between the Variables
  • 11.4.1.3 Generating Heat Map of the Correlation
  • 11.4.2 Dataset 2 (https://raw.githubusercontent.com/nytimes/covid-19-data/master/us-counties.csv)
  • 11.4.2.1 Snapshot of the dataset
  • 11.4.2.2 Generating Pair Plot
  • 11.5 Pattern Prediction of Covid-19 Using Machine Learning Approaches
  • 11.6 Conclusions
  • References.

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