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Artificial intelligence in COVID-19 / / Niklas Lidströmer and Yonina C. Eldar, editors
| Artificial intelligence in COVID-19 / / Niklas Lidströmer and Yonina C. Eldar, editors |
| Pubbl/distr/stampa | Cham, Switzerland : , : Springer, , [2022] |
| Descrizione fisica | 1 online resource (346 pages) |
| Disciplina | 362.1962414 |
| Soggetto topico |
COVID-19 (Disease) - Data processing
Medical informatics Pandemics - Economic aspects COVID-19 Epidèmies Processament de dades Informàtica mèdica |
| Soggetto genere / forma | Llibres electrònics |
| ISBN | 3-031-08506-X |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Intro -- 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. |
| Record Nr. | UNINA-9910624309003321 |
| Cham, Switzerland : , : Springer, , [2022] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Compressed sensing : theory and applications / / edited by Yonina C. Eldar, Gitta Kutyniok [[electronic resource]]
| Compressed sensing : theory and applications / / edited by Yonina C. Eldar, Gitta Kutyniok [[electronic resource]] |
| Pubbl/distr/stampa | Cambridge : , : Cambridge University Press, , 2012 |
| Descrizione fisica | 1 online resource (xii, 544 pages) : digital, PDF file(s) |
| Disciplina | 621.382/2 |
| Soggetto topico |
Signal processing
Wavelets (Mathematics) Compressed sensing (Telecommunication) |
| ISBN |
1-107-22736-4
1-280-77350-2 0-511-79430-4 9786613684271 1-139-33754-8 1-139-33999-0 1-139-34157-X 1-139-33841-2 1-139-33667-3 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | ; 1. Introduction to compressed sensing / Mark A. Davenport, Marco F. Duarte, Yonina C. Eldar, and Gitta Kutyniok -- ; 2. Second-generation sparse modeling : structured and collaborative signal analysis / Alexey Castrodad, Ignacio Ramirez, Guillermo Sapiro, Pablo Sprechmann, and Guoshen Yu -- ; 3. Xampling : compressed sensing of analog signals / Moshe Mishali and Yonina C. Eldar -- ; 4. Sampling at the rate of innovation : theory and applications / Jose Antonia Urigüen, Yonina C. Eldar, Pier Luigi Dragotta, and Zvika Ben-Haim -- ; 5. Introduction to the non-asymptotic analysis of random matrices / Roman Vershynin -- ; 6. Adaptive sensing for sparse recovery / Jarvis Haupt and Robert Nowak -- ; 7. Fundamental thresholds in compressed sensing : a high-dimensional geometry approach / Weiyu Xu and Babak Hassibi -- ; 8. Greedy algorithms for compressed sensing / Thomas Blumensath, Michael E. Davies, and Gabriel Rilling -- ; 9. Graphical models concepts in compressed sensing / Andrea Montanari -- ; 10. Finding needles in compressed haystacks / Robert Calderbank and Sina Jafarpour -- ; 11. Data separation by sparse representations / Gitta Kutyniok -- ; 12. Face recognition by sparse representation / Arvind Ganesh, Andrew Wagner, Zihan Zhou, Allen Y. Yang, Yi Ma, and John Wright. |
| Record Nr. | UNINA-9910462405003321 |
| Cambridge : , : Cambridge University Press, , 2012 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Compressed sensing : theory and applications / / edited by Yonina C. Eldar, Gitta Kutyniok [[electronic resource]]
| Compressed sensing : theory and applications / / edited by Yonina C. Eldar, Gitta Kutyniok [[electronic resource]] |
| Pubbl/distr/stampa | Cambridge : , : Cambridge University Press, , 2012 |
| Descrizione fisica | 1 online resource (xii, 544 pages) : digital, PDF file(s) |
| Disciplina | 621.382/2 |
| Soggetto topico |
Signal processing
Wavelets (Mathematics) Compressed sensing (Telecommunication) |
| ISBN |
1-107-22736-4
1-280-77350-2 0-511-79430-4 9786613684271 1-139-33754-8 1-139-33999-0 1-139-34157-X 1-139-33841-2 1-139-33667-3 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | ; 1. Introduction to compressed sensing / Mark A. Davenport, Marco F. Duarte, Yonina C. Eldar, and Gitta Kutyniok -- ; 2. Second-generation sparse modeling : structured and collaborative signal analysis / Alexey Castrodad, Ignacio Ramirez, Guillermo Sapiro, Pablo Sprechmann, and Guoshen Yu -- ; 3. Xampling : compressed sensing of analog signals / Moshe Mishali and Yonina C. Eldar -- ; 4. Sampling at the rate of innovation : theory and applications / Jose Antonia Urigüen, Yonina C. Eldar, Pier Luigi Dragotta, and Zvika Ben-Haim -- ; 5. Introduction to the non-asymptotic analysis of random matrices / Roman Vershynin -- ; 6. Adaptive sensing for sparse recovery / Jarvis Haupt and Robert Nowak -- ; 7. Fundamental thresholds in compressed sensing : a high-dimensional geometry approach / Weiyu Xu and Babak Hassibi -- ; 8. Greedy algorithms for compressed sensing / Thomas Blumensath, Michael E. Davies, and Gabriel Rilling -- ; 9. Graphical models concepts in compressed sensing / Andrea Montanari -- ; 10. Finding needles in compressed haystacks / Robert Calderbank and Sina Jafarpour -- ; 11. Data separation by sparse representations / Gitta Kutyniok -- ; 12. Face recognition by sparse representation / Arvind Ganesh, Andrew Wagner, Zihan Zhou, Allen Y. Yang, Yi Ma, and John Wright. |
| Record Nr. | UNINA-9910790352603321 |
| Cambridge : , : Cambridge University Press, , 2012 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Integrated Sensing and Communications / / edited by Fan Liu, Christos Masouros, and Yonina C. Eldar
| Integrated Sensing and Communications / / edited by Fan Liu, Christos Masouros, and Yonina C. Eldar |
| Edizione | [First edition.] |
| Pubbl/distr/stampa | Singapore : , : Springer, Springer Nature Singapore Pte Ltd, , [2023] |
| Descrizione fisica | 1 online resource (609 pages) |
| Disciplina | 621.382 |
| Soggetto topico | Telecommunication |
| ISBN | 981-9925-01-0 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Background, Motivation, Applications, and Preliminaries for ISAC -- Fundamental Limits for ISAC - Communication Perspective -- Fundamental Limits for ISAC - Radar Perspective -- Fundamental Limits for ISAC - Localization Perspective -- Fundamental Limits for ISAC - Asymptotic Analysis -- PHY Tradeoff and Resource Allocation for ISAC -- Sensing-Centric ISAC Signal Processing -- Communication-Centric ISAC Signal Processing -- Joint ISAC Signal Processing -- ISAC Receiver Design -- MmWave and THz ISAC Signal Processing -- Radar-Communication Spectrum Sharing -- Perceptive Cellular Network -- (Device-Free) Sensing-Assisted Communications -- Wi-Fi Sensing -- Automotive ISAC: V2I Networks -- Automotive ISAC: V2V Networks and SLAM -- Security and Privacy in ISAC -- Full-duplex ISAC -- ISAC RF Front-End Design -- ISAC With Emerging Communication Technologies. |
| Record Nr. | UNINA-9910735785603321 |
| Singapore : , : Springer, Springer Nature Singapore Pte Ltd, , [2023] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||