LEADER 11568nam 2200649 450 001 9910624309003321 005 20240223125636.0 010 $a3-031-08506-X 035 $a(MiAaPQ)EBC7134119 035 $a(Au-PeEL)EBL7134119 035 $a(CKB)25299476500041 035 $a(OCoLC)1350687369 035 $a(PPN)266354475 035 $a(EXLCZ)9925299476500041 100 $a20230320d2022 uy 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 00$aArtificial intelligence in COVID-19 /$fNiklas Lidstro?mer and Yonina C. Eldar, editors 210 1$aCham, Switzerland :$cSpringer,$d[2022] 210 4$d©2022 215 $a1 online resource (346 pages) 311 08$aPrint version: Lidströmer, Niklas Artificial Intelligence in Covid-19 Cham : Springer International Publishing AG,c2022 9783031085055 320 $aIncludes bibliographical references and index. 327 $aIntro -- 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. 327 $aFuture 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. 327 $aAppendix 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. 327 $aSome 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. 327 $aDetecting 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. 327 $aAt the Population Level, Health Surveillance Systems and AI. 606 $aCOVID-19 (Disease)$xData processing 606 $aMedical informatics 606 $aPandemics$xEconomic aspects 606 $aCOVID-19$2thub 606 $aEpidèmies$2thub 606 $aProcessament de dades$2thub 606 $aInformàtica mèdica$2thub 608 $aLlibres electrònics$2thub 615 0$aCOVID-19 (Disease)$xData processing. 615 0$aMedical informatics. 615 0$aPandemics$xEconomic aspects. 615 7$aCOVID-19 615 7$aEpidèmies 615 7$aProcessament de dades 615 7$aInformàtica mèdica 676 $a362.1962414 702 $aLidstro?mer$b Niklas 702 $aEldar$b Yonina C. 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910624309003321 996 $aArtificial Intelligence in Covid-19$92967940 997 $aUNINA