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001451136 001__ 1451136
001451136 003__ OCoLC
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001451136 019__ $$a1350687369
001451136 020__ $$a9783031085062$$q(electronic bk.)
001451136 020__ $$a303108506X$$q(electronic bk.)
001451136 020__ $$z9783031085055
001451136 020__ $$z3031085051
001451136 0247_ $$a10.1007/978-3-031-08506-2$$2doi
001451136 035__ $$aSP(OCoLC)1350669581
001451136 040__ $$aYDX$$beng$$erda$$epn$$cYDX$$dGW5XE$$dEBLCP$$dUKAHL$$dOCLCQ
001451136 049__ $$aISEA
001451136 050_4 $$aRA644.C67
001451136 08204 $$a362.1962/4140028563$$223/eng/20221122
001451136 24500 $$aArtificial intelligence in COVID-19 /$$cNiklas Lidströmer, Yonina C. Eldar, editors.
001451136 264_1 $$aCham :$$bSpringer,$$c[2022]
001451136 264_4 $$c©2022
001451136 300__ $$a1 online resource (xii, 340 pages) :$$billustrations (some color)
001451136 336__ $$atext$$btxt$$2rdacontent
001451136 337__ $$acomputer$$bc$$2rdamedia
001451136 338__ $$aonline resource$$bcr$$2rdacarrier
001451136 500__ $$aIncludes index.
001451136 5050_ $$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
001451136 5058_ $$aGroup Testing Methods for COVID-19 -- Adaptive GT Methods -- Non-Adaptive GT Methods -- Pooling Matrix -- Noiseless Linear Non-Adaptive Recovery -- Noisy Non-Linear Non-Adaptive Recovery -- Summary -- Compressed Sensing for Pooled Testing for COVID-19 -- Compressed Sensing Forward Model for Pooled RT-PCR -- CS Algorithms for Recovery -- Details of Algorithms -- Assessment of Algorithm Performance and Experimental Protocols -- Choice of Pooling Matrices -- Choice of Number of Pools -- Use of Side Information in Pooled Inference -- Comparative Discussion and Summary -- References
001451136 5058_ $$aChapter 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
001451136 5058_ $$aIDentif.AI as a Pandemic Preparedness Platform -- Use of Real-World Data to Identify Potential Targets for Drug Repurposing -- Future Directions -- References -- Chapter 4: AI and Point of Care Image Analysis for COVID-19 -- Introduction -- Motivation for Using Imaging -- Motivation for Using AI with Imaging -- Integration of Imaging with Other Modalities -- Literature Overview -- Chest X-Ray Imaging -- Diagnosis Models -- Prognosis Models -- Use of Longitudinal Imaging -- Fusion with Other Data Modalities -- Common Issues with AI and Chest X-Ray Imaging -- Duplication and Quality Issues
001451136 5058_ $$aSource 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
001451136 506__ $$aAccess limited to authorized users.
001451136 520__ $$aThis 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.
001451136 588__ $$aOnline resource; title from PDF title page (SpringerLink, viewed November 22, 2022).
001451136 650_0 $$aCOVID-19 (Disease)$$xData processing.
001451136 650_0 $$aPandemics$$xData processing.
001451136 650_0 $$aMedical informatics.
001451136 655_0 $$aElectronic books.
001451136 7001_ $$aLidströmer, Niklas,$$eeditor.
001451136 7001_ $$aEldar, Yonina C.,$$eeditor.$$1https://isni.org/isni/0000000078129577
001451136 77608 $$iPrint version: $$z3031085051$$z9783031085055$$w(OCoLC)1317841714
001451136 852__ $$bebk
001451136 85640 $$3Springer Nature$$uhttps://univsouthin.idm.oclc.org/login?url=https://link.springer.com/10.1007/978-3-031-08506-2$$zOnline Access$$91397441.1
001451136 909CO $$ooai:library.usi.edu:1451136$$pGLOBAL_SET
001451136 980__ $$aBIB
001451136 980__ $$aEBOOK
001451136 982__ $$aEbook
001451136 983__ $$aOnline
001451136 994__ $$a92$$bISE