| Description |
1 online resource. |
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text txt rdacontent |
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computer c rdamedia |
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online resource cr rdacarrier |
| Series |
Intelligent data centric systems
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| Note |
Print version record. |
| Summary |
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 universities and medical schools must include these topics and case studies in their usual courses of health informatics to keep up with the pace of technological and medical advancements. This book will also serve professors teaching courses and industry practitioners and professionals working in the R&D team of leading medical and informatics companies who want to embrace AI and eHealth to fight COVID-19. Since AI in healthcare is a comparatively new field, there exists a vacuum of literature in this field, especially when applied to COVID-19. With the area of AI in COVID-19 being quite young, students and researchers usually face a struggle to rely on the few published papers (which are obviously too specific) or whitepapers by tech-giants (which are too wide). Discusses the fundamentals and theoretical concepts of applying AI in healthcare pertaining to COVID-19 Provides a landscape view to the applied aspect of AI in healthcare related COVID-19 through case studies and innovative applications Discusses key concerns and challenges in the field of AI in eHealth during the pandemic, along with other allied fields like IoT, creating a broad platform of transdisciplinary discussion. |
| Contents |
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. |
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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. |
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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. |
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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. |
| Subject |
Artificial intelligence -- Medical applications.
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COVID-19 (Disease) -- Data processing.
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Intelligence artificielle -- Applications en médecine.
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COVID-19 -- Informatique.
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Artificial intelligence -- Medical applications
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COVID-19 (Disease) -- Data processing
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| Other Form: |
Print version: 9780323905732 |
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Print version: 0323905315 9780323905312 (OCoLC)1348634824 |
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Print version: ARTIFICIAL INTELLIGENCE IN HEALTHCARE AND COVID-19. [S.l.] : ELSEVIER ACADEMIC PRESS, 2023 0323905315 (OCoLC)1348634824 |
| ISBN |
9780323905732 electronic book |
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0323905730 electronic book |
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9780323905312 |
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0323905315 |
| Standard No. |
AU@ 000074417561 |
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AU@ 000074451236 |
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UKMGB 021026788 |
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