Artificial Intelligence in Covid-19

Foreword
Preface
Contents
About the Editors
Chapter 1: Introduction to Artificial Intelligence in COVID-19
	Pandemics
	History of Pandemics
	The COVID-19 Pandemic
	Origins of the COVID-19 Pandemic
	Continuous Fight for Science and Reason
	Modern Tools for Pandemic Control
	A Brief Chronology of the Chapters of This Book
	Power of Science
	References
Chapter 2: AI for Pooled Testing of COVID-19 Samples
	Introduction
	System Model
		The PCR Process
		Mathematical Model
		Pooled COVID-19 Tests
		Recovery from Pooled Tests
	Group Testing Methods for COVID-19
		Adaptive GT Methods
		Non-Adaptive GT Methods
			Pooling Matrix
			Noiseless Linear Non-Adaptive Recovery
			Noisy Non-Linear Non-Adaptive Recovery
		Summary
	Compressed Sensing for Pooled Testing for COVID-19
		Compressed Sensing Forward Model for Pooled RT-PCR
		CS Algorithms for Recovery
			Details of Algorithms
		Assessment of Algorithm Performance and Experimental Protocols
		Choice of Pooling Matrices
		Choice of Number of Pools
	Use of Side Information in Pooled Inference
	Comparative Discussion and Summary
	References
Chapter 3: AI for Drug Repurposing in the Pandemic Response
	Introduction
	Desirable Features of AI for Drug Repurposing in Pandemic Response
		Technical Flexibility and Efficiency
		Clinical Applicability and Acceptability
	Major AI Applications for Drug Repurposing in Response to COVID-19
		Knowledge Mining
		Network-Based Analysis
		In Silico Modelling
	IDentif.AI Platform for Rapid Identification of Drug Combinations
		Project IDentif.AI
		IDentif.AI for Drug Optimization Against SARS-CoV-2
		IDentif.AI 2.0 Platform in an Evolving Pandemic
		IDentif.AI as a Pandemic Preparedness Platform
	Use of Real-World Data to Identify Potential Targets for Drug Repurposing
	Future Directions
	References
Chapter 4: AI and Point of Care Image Analysis for COVID-19
	Introduction
		Motivation for Using Imaging
		Motivation for Using AI with Imaging
		Integration of Imaging with Other Modalities
		Literature Overview
	Chest X-Ray Imaging
		Diagnosis Models
		Prognosis Models
		Use of Longitudinal Imaging
		Fusion with Other Data Modalities
		Common Issues with AI and Chest X-Ray Imaging
			Duplication and Quality Issues
			Source Issues
			Frankenstein Datasets
			Implicit Biases in the Source Data
			Artificial Limitations Due to Transfer Learning
	Computed Tomography Imaging
		Diagnosis Models
		Prognosis Models
		Applications to Regions Away from the Lungs
		Use of Longitudinal Imaging
		Fusion with Other Data Modalities
		Common Issues with AI and Computed Tomography Imaging
	Ultrasound Imaging
		What Can be Observed in LUS
		Models Assisting in Interpreting LUS
		Diagnosis Models
		Prognosis Models
		Use of Longitudinal Imaging
		Common Issues with AI and Ultrasound Imaging
	Conclusions
		Success Stories
		Pitfalls to Focus On
		Lessons Learned and Recommendations
		The Next Pandemic
	References
Chapter 5: Machine Learning and Laboratory Values in the Diagnosis, Prognosis and Vaccination Strategy of COVID-19
	Introduction
	COVID-19, Machine Learning and Laboratory Values: The State of the Art
	Literature Search Results
		Diagnostic Studies
		Prognostic Studies
	Considerations on the Literature Reviewed
		Heterogeneity in Patient Selection
		Laboratory Parameters Used by Machine Learning Models
		Types of Models and Their Validation
		Model Implementation
	The Role of Artificial Intelligence in the Vaccination Strategy Against SARS-COV-2 Through Laboratory Tests
		Real-World Vaccination Strategies
		Artificial Intelligence Potentialities
	Conclusions
	Appendix 1
		Diagnostic Papers (D)
		Prognostic Papers (P)
	Appendix 2: Tool Online
	References
Chapter 6: AI and the Infectious Medicine of COVID-19
	Introduction
	AI and ML for SARS-CoV-2 Early Research Using Pathogen Sequence Data
	AI and ML for Research of SARS-CoV-2 Antivirals
	AI and ML for COVID-19 Infectious Medicine Early Research Using Language Data
	AI and ML in Real World Data Analysis of COVID-19
	AI and ML in Molecular Diagnostics of COVID-19
	AI and ML in Image-Based Diagnostics of COVID-19 and Clinical Decision Support
	AI and ML in COVID-19 Medical Care
		Prevention, Infection Risk and Epidemiology
		Treatment and Prognosis
	Conclusions
	References
Chapter 7: AI and ICU Monitoring on the Wake of the COVID-19 Pandemic
	Introduction
	ICU Monitoring Through AI
	ICU Monitoring and AI in Pre-pandemic Times
	The Impact of the COVID-19 Pandemic on the ICU and the Role of AI
	Conclusions
	References
Chapter 8: Symptom Based Models of COVID-19 Infection Using AI
	Introduction
	Using Machine Learning Methods to Determine Mortality of Patient with COVID-19
	Using Machine Learning Methods to Detect the Presence of COVID-19 Infection
	Using Machine Learning Methods to Differentiate COVID-19 and Influenza/Common Cold Infections
	Summary, Limitations, Challenges, and Future Applications
	References
Chapter 9: AI Techniques for Forecasting Epidemic Dynamics: Theory and Practice
	Introduction
	A Review of Model Types and Limits to Forecasting
	Preliminaries
		Model Details
		Metrics for Forecast Evaluations
	AI-Driven Engineering
		An Example of a Real-time Forecasting Model
			Results
		A GNN-Based Spatio-Temporal Model
			Additional Details Regarding the Framework
			Forecasting Performance
	Theoretical Foundations for Forecasting in Network Models
		Overview
		Some Short-Term Forecasting Problems and Their Computational Intractability
	Discussion
	References
Chapter 10: Regulatory Aspects on AI and Pharmacovigilance for COVID-19
	What Does Artificial Intelligence Mean According to Legal Definition?
	AI and Health
	The European Union Legal Framework: A Work in Progress
	The Proposed EU Regulation (Artificial Intelligence Act)
	The Use of AI in Research and Developing Medicinal Products and Monitoring Their Quality, Safety and Efficacy
	The Added Value Brought Using Artificial Intelligence in Performing Pharmacovigilance Activities in General and During the COVID-19 Pandemic
	Ethical Issues: A Few Caveats
	The Personal Data Protection Implications
	Provisional Conclusions
	Suggested Reading
Chapter 11: AI and the Clinical Immunology/Immunoinformatics for COVID-19
	Introduction
	Challenge for Traditional Vaccines in COVID-19
		Long Development and Design Period
		Difficulties in Knowing and Optimizing the Efficacy and Side Effects
		Uncertainties with the Development and Other Costs During Production, Storage, and Transportation
		Hard to Tackle Unknown and Emerging Mutations of Viruses
	Existing AI Techniques Help the Traditional Vaccine Development in COVID-19
		AI Makes the Practical Experimental Results Computational
		AI-Based Computational Tools Can Help the Traditional Vaccine Design
	AI-Based In Silico Vaccine Design
	Our Recently Proposed DeepVacPred Vaccine Design Framework
	Artificial Intelligence for Investigating Viral Evolution and Mutations
		An Algorithmic Information Theoretic Approach to Discover the State Machine Generator Governing the Viral Sequence Structure and Enabling AI Strategies for Viral Mutation Prediction
		Characterizing the Temporal Evolution of SARS-CoV-2 in a Continuous Manner
		Detecting Regions Within Viral Sequences Likely to Exhibit Mutations
	Summary
	References
Chapter 12: AI and Dynamic Prediction of Deterioration in Covid-19
	Introduction
	COVID-19: A Novel Disease—Usage of Newer or Older Clinical Decisions Support Systems?
	Clinical Decisions Support System Stable Parameters/Features Using Threshold Values
		Patient Deterioration
		General Prediction Scores
		Early Warning Systems (EWS)
	AI for Prediction of Deterioration
	AI Assisted Patient-Specific Risk Prediction
	AI Assisted Prediction of Critical Illness and Deterioration in COVID-19 Patients
		Mortality Prediction Models for Covid-19
		Mortality Prediction Models Using High-Frequency Data
		Prediction Models for Sepsis
	Explainable and Interpretable Machine Learning Methods for Clinical Decision Support Systems
	References
Chapter 13: AI, Epidemiology and Public Health in the Covid Pandemic
	Introduction
	Epidemiology: Definition and Purposes
	Epidemiology and Public Health: How They Relate to Each Other and the Concept of One Health
		Individual Health and Population Health
		The Articulation Between Individual and Population Level
		Biomedical and Biopsychosocial Models of Health: Individual, Environmental and Social Determinants of Health
		From Precision Medicine to Precision Public Health
	Epidemiology and Public Health in the Digital Era: Prerequisites
		A Ubiquitous Digitization
		The Evolutions of the Regulatory Framework on Personal Data
		Connected Devices and Equipment Rates
		Digital and E-health Literacy
		Towards a Real Life Use of AI in Epidemiology and Public Health: Some First Examples
			No Data Means No Artificial Intelligence: A Few Words About Data Federation and “New” Types of Data
			Citizens and Patients as Producers, Actor and Manager of Their Own Health
			At the Population Level, Health Surveillance Systems and AI
			Between the Individual and the Population, Healthcare Systems: Learning Healthcare Systems (LHS)
	What Contributions Could Be Expected from AI in Epidemiology and Public Health in the Context of a Pandemic?
		What Is Due to the Use of Non-classical Data Sources, and to the Comparison or Cross-Checking Between Sources
		The Real-Time or Refreshable Nature of the Information
		New Types of Analysis, in Particular on Massive, Incomplete or Even Poorly Balanced Data
		Aid in the Search for Causality or Networks of Causality
		Evaluation Methods Based on Observation or Quasi-Experimental, as for Virtual Clinical Trials
		The Augmented Expert in Public Health
	Some Specificities and Examples in the Context of the Pandemic: Epidemic Modeling, Public Health and Counting Deaths
		Epidemic Modeling: How to Retrieve Accurate and Timely Data to Feed a Model
		Counting Deaths in the Context of a Health Crisis Based on Information Systems and AI
			Challenges of Counting Death, the Case of the French System
			Certification of Deaths and Collection of Death Certificates, Specificity to Emerging Diseases Such as COVID
			Issues in Coding Causes of Death, Especially in Emerging Diseases
			AI’s Place in Coding Deaths on Emerging Causes/Diseases, COVID Contextual Example
			To Take Away
	AI at the Service of Epidemiology and Public Health in the Context of the Covid19 Pandemic
		Bibliographic Search
		A Typology of AI Use at the Service of Epidemiology and Public Health in the Context of the Pandemic
			Outbreak Monitoring
			Epidemiologic Outcomes and Characteristics Discovering
			Social Control and Monitoring
			Assisted/Augmented Scientific Research and Knowledge Sharing
			Healthcare Resources Adaptation and Optimization
			Social, Economic and Governmental Measures Assessment
			Infodemics
		What Performance of AI in the Uses Identified in Epidemiology and Public Health?
			Outbreak Monitoring
			Epidemiologic Outcomes and Characteristics Discovering
			Social Control and Monitoring
			Assisted/Augmented Scientific Research and Knowledge Sharing
			Healthcare Resources Adaptation and Optimization
			Social, Economic and Government Measures Assessment
			Infodemics
		Beyond Performance, What About the Degree of Maturity of Published AI Algorithms?
			Outbreak Monitoring
			Epidemiologic Outcomes and Characteristics Discovering
			Social Control and Monitoring
			Assisted/Augmented Scientific Research and Knowledge Sharing
			Healthcare Resources Adaptation and Optimization
			Social, Economic and Government Measures Assessment
			Infodemics
	Two Years of Pandemic: Lessons for Epidemiology and the Place of AI
		Many AI Applications for Epidemiology and Public Health in the Context of the Pandemic: Yet Still Evidence for Their Reliability and Usefulness to Bring
		“Lancetgate”: A Lesson About Identified Risks of Massive Data Collection and Reuse and Its Consequences on Public Health Decisions
		What AI Has to Learn from Epidemiology and Public Health?
		Predicting vs. Explaining: Is It Reconcilable? Is Explainable AI Necessary in Epidemiology?
		The Status of the Whistleblower in the Case of Emerging Diseases: The Hybrids of Simondon and Latour
		Towards a Potentially More Actionable and Precise Public Health: The Challenges of Regulation, Ethics and Ecology on an International Level
			Benefice/Risk Balance of the Use of AI in the Context of Population Management
			The Need for a Cautious Analysis of the Real Cost of AI Use in Health
	References
Index