1 option
Artificial intelligence in COVID-19 / Niklas Lidströmer, Yonina C. Eldar, editors.
- Format:
- Book
- Language:
- English
- Subjects (All):
- COVID-19 (Disease)--Data processing.
- COVID-19 (Disease).
- Pandemics--Data processing.
- Pandemics.
- Medical informatics.
- Genre:
- Electronic books.
- Physical Description:
- 1 online resource (xii, 340 pages) : illustrations (some color)
- Place of Publication:
- Cham : Springer, [2022]
- Summary:
- This book deals with the advantages of using artificial intelligence (AI) in the fight against the COVID-19 and against future pandemics that could threat humanity and our environment. This book is a practical, scientific and clinically relevant example of how medicine and mathematics will fuse in the 2020s, out of external pandemic pressure and out of scientific evolutionary necessity. This book contains a unique blend of the world's leading researchers, both in medicine, mathematics, computer science, clinical and preclinical medicine, and presents the research front of the usage of AI against pandemics. Equipped with this book the reader will learn about the latest AI advances against COVID-19, and how mathematics and algorithms can aid in preventing its spreading course, treatments, diagnostics, vaccines, clinical management and future evolution.
- Contents:
- 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
- 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
- Chapter 4: AI and Point of Care Image Analysis for COVID-19
- 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
- Applications to Regions Away from the Lungs
- Common Issues with AI and Computed Tomography Imaging
- Ultrasound Imaging
- What Can be Observed in LUS
- Models Assisting in Interpreting LUS
- Common Issues with AI and Ultrasound Imaging
- Conclusions
- Notes:
- Includes index.
- Online resource; title from PDF title page (SpringerLink, viewed November 22, 2022).
- ISBN:
- 9783031085062
- 303108506X
- OCLC:
- 1350669581
- Access Restriction:
- Restricted for use by site license.
The Penn Libraries is committed to describing library materials using current, accurate, and responsible language. If you discover outdated or inaccurate language, please fill out this feedback form to report it and suggest alternative language.