Artificial intelligence in healthcare and COVID-19

Artificial intelligence in healthcare and COVID-19

Artificial Intelligence in Healthcare and COVID-19 showcases theoretical concepts and implementational and research perspectives surrounding AI. The book addresses both medical and technological visions, making it even more applied. With the advent of COVID-19, it is obvious that leading universitie...

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Detalles Bibliográficos
Otros Autores: Chatterjee, Parag, 1992- editor (editor), Esposito, Massimo, editor
Formato: Libro electrónico
Idioma:Inglés
Publicado: London, England : Elsevier Inc [2023]
Colección:Intelligent Data-Centric Systems Series
Materias:
Artificial intelligence > Medical applications.
COVID-19 (Disease) > Data processing.
Ver en Biblioteca Universitat Ramon Llull:https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009835432406719
Tabla de Contenidos:
  • Front Cover
  • Artificial Intelligence in Healthcare and COVID-19
  • Copyright Page
  • Contents
  • List of contributors
  • Preface
  • 1 Improvement of mental health of frontline healthcare workers during COVID-19 pandemic using artificial intelligence
  • Other notes
  • 1.1 Introduction
  • 1.2 Background
  • 1.3 Main content
  • 1.4 Methodologies and implementation
  • 1.5 Discussion
  • 1.5.1 Connection to artificial intelligence
  • 1.5.2 Strengths
  • 1.5.3 Weaknesses
  • 1.6 Conclusion
  • References
  • 2 Effective algorithms for solving statistical problems posed by COVID-19 pandemic
  • 2.1 Introduction
  • 2.2 Forecasting the epidemic curves of coronavirus
  • 2.2.1 Forecasting models for the COVID-19 outbreak
  • 2.3 Nonparametric tests used for forecasting models estimation
  • 2.3.1 Nonparametric tests for homogeneity
  • 2.3.2 Exact nonparametric test for homogeneity
  • 2.4 Comparison of forecast models
  • 2.5 Conclusion and scope for the future work
  • References
  • 3 Reconsideration of drug repurposing through artificial intelligence program for the treatment of the novel coronavirus
  • 3.1 Introduction
  • 3.2 Viral morphology
  • 3.2.1 Structured proteins
  • 3.2.1.1 Spike protein/spike membrane
  • 3.2.1.2 Membranous proteins
  • 3.2.1.3 Nucleic acid-protein/nucleocapsid
  • 3.2.1.4 Enveloped protein
  • 3.2.2 Nonstructured proteins
  • 3.2.2.1 Proteases
  • 3.2.2.2 RNA-dependent polymerase
  • 3.2.2.3 Helicase
  • 3.3 Virus lifecycle
  • 3.3.1 Life process of severe acute respiratory syndrome 2
  • 3.3.1.1 Attachment and entry
  • 3.3.1.2 Replication and transcription
  • 3.3.1.3 Assembly and release
  • 3.4 Currently available viral targeting drug candidates at various stages of life cycle
  • 3.5 Different drug repurposing approaches
  • 3.5.1 Target approach
  • 3.5.2 Knowledge-dependent approach
  • 3.5.3 Molecular docking-based approach.
  • 3.5.4 Machine learning approaches
  • 3.5.5 Pathway-based approaches
  • 3.5.6 Artificial neuronal network approaches
  • 3.5.7 Deep learning machine approaches
  • 3.5.8 Network modeling approach
  • 3.5.8.1 Autoencoder approaches
  • 3.5.8.2 Text mining approaches
  • 3.6 Artificial intelligence algorithms for drug repurposing
  • 3.7 Computational intelligence-based approaches to identify therapeutic candidates for repurposing against coronavirus
  • 3.7.1 Network-based model
  • 3.7.2 Structure-based approaches
  • 3.7.3 Artificial intelligence approaches
  • 3.8 Challenges in drug repurposing
  • 3.9 Future perspectives of artificial intelligence-informed drug repurposing
  • 3.10 Conclusion
  • References
  • 4 COVID-19: artificial intelligence solutions, prediction with country cluster analysis, and time-series forecasting
  • 4.1 Introduction
  • 4.1.1 Motivation for this study
  • 4.1.2 Adverse impacts of COVID-19 outbreak
  • 4.1.3 Chapter organization
  • 4.1.4 Table of acronyms used in this chapter
  • 4.2 Review of literature on COVID-19 pandemic
  • 4.3 K-means clustering for COVID-19 country analysis
  • 4.3.1 Cluster analysis: an overview
  • 4.3.2 Dataset selection and preprocessing
  • 4.3.3 Findings from COVID-19 country cluster data analysis
  • 4.3.4 The results and discussions
  • 4.4 Time-series modeling for COVID-19 new cases
  • 4.4.1 Time-series modeling: an overview
  • 4.4.2 Dataset description
  • 4.4.3 Time-series exploration
  • 4.4.4 Predictive analytics
  • 4.5 Conclusion
  • References
  • Further reading
  • 5 Graph convolutional networks for pain detection via telehealth
  • 5.1 Introduction
  • 5.2 Methodology
  • 5.2.1 Features extraction
  • 5.2.2 Graph-based modules
  • 5.2.3 Frame-wise weight calculation
  • 5.2.4 Classification
  • 5.3 Experiments
  • 5.3.1 Datasets
  • 5.3.2 Experimental setting
  • 5.4 Results and discussion
  • 5.5 Conclusion.
  • Acknowledgment
  • References
  • 6 The role of social media in the battle against COVID-19
  • 6.1 Introduction
  • 6.2 Materials and methods
  • 6.3 Related reviews
  • 6.4 Understanding COVID-19 data
  • 6.4.1 Topic detection
  • 6.4.2 Sentiment analysis
  • 6.5 Misinformation identification and spreading
  • 6.6 COVID-19 forecasting
  • 6.7 Discussion: challenges and future directions
  • 6.8 Conclusion
  • References
  • 7 De-identification techniques to preserve privacy in medical records
  • 7.1 Introduction
  • 7.2 Background
  • 7.2.1 Deep learning systems
  • 7.2.2 Language models and embeddings
  • 7.2.3 Clinical de-identification, low-resource languages, and transfer learning
  • 7.3 Material and methods
  • 7.3.1 Data sets
  • 7.3.1.1 The SIRM COVID-19 de-identification corpus
  • 7.3.1.2 The i2b2/UTHealth 2014 de-identification corpus
  • 7.3.2 System architectures
  • 7.3.2.1 BiLSTM plus CRF-based architecture
  • 7.3.2.1.1 Embedding layer
  • 7.3.2.2 BERT-based architecture
  • 7.3.3 Experimental setups
  • 7.3.3.1 BiLSTM plus CRF-based systems
  • 7.3.3.2 BERT-based systems
  • 7.3.4 Evaluation metrics
  • 7.3.5 Training strategies
  • 7.4 Results and discussion
  • 7.5 Conclusion
  • References
  • 8 Estimation of COVID-19 fatality associated with different SARS-CoV-2 variants
  • 8.1 Introduction
  • 8.1.1 Related work
  • 8.2 Materials and methods
  • 8.2.1 Data on COVID-19 infections and deaths
  • 8.2.2 Data about SARS-CoV-2 variants
  • 8.2.3 Models to estimate fatality
  • 8.2.4 Uncertainty of available data and fatality estimation
  • 8.2.5 Correlation with vaccine distribution
  • 8.2.6 Hypotheses to generalize conclusions
  • 8.3 Results
  • 8.4 Discussion and conclusion
  • References
  • 9 Artificial intelligence for chest imaging against COVID-19: an insight into image segmentation methods
  • 9.1 Introduction
  • 9.2 Chest CT findings of COVID-19 pneumonia.
  • 9.3 Medical image segmentation and artificial intelligence
  • 9.3.1 The fourth generation of segmentation methods: deep learning approaches
  • 9.3.2 Evaluation metrics
  • 9.4 Existing methods for COVID-19 chest CT images segmentation
  • 9.4.1 Lung-region-oriented methods
  • 9.4.2 Lung-lesion-oriented methods
  • 9.4.2.1 Binary lung lesion methods
  • 9.4.2.2 Multi-class lung lesion methods
  • 9.5 Attention-FCNN: a novel DL model for the segmentation of COVID-19 chest CT scans
  • 9.5.1 Chest CT imaging dataset
  • 9.5.2 Attention-FCNN architecture
  • 9.5.3 Attention gates: structure and functioning
  • 9.5.4 Training details
  • 9.5.5 Results
  • 9.5.6 Ablation study
  • 9.6 Discussion and conclusions
  • References
  • Index
  • Back Cover.

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