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Deep learning in personalized healthcare and decision support /

Deep Learning in Personalized Healthcare and Decision Support discusses the potential of deep learning technologies in the healthcare sector.The book covers the application of deep learning tools and techniques in diverse areas of healthcare, such as medical image classification, telemedicine, clini...

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Bibliographic Details
Corporate Author: ScienceDirect (Online service)
Other Authors: Garg, Harish, Chatterjee, Jyotir Moy
Format: eBook
Language:English
Published: London, UK ; San Diego, CA : Academic Press, 2023.
Subjects:
Precision medicine > Data processing.
Deep learning (Machine learning)
Artificial intelligence > Medical applications.
Deep Learning
Médecine de précision > Informatique.
Apprentissage profond.
Intelligence artificielle > Applications en médecine.
Artificial intelligence > Medical applications
Electronic books.
Online Access:Connect to the full text of this electronic book
Table of Contents:
  • Front Cover
  • Deep Learning in Personalized Healthcare and Decision Support
  • Deep Learning in Personalized Healthcare and Decision Support
  • Copyright
  • Contents
  • Contributors
  • Preface
  • Acknowledgments
  • 1
  • The future of health diagnosis and treatment: an exploration of deep learning frameworks and innovative applica ...
  • 1. Introduction
  • 2. Computational deep learning frameworks for health monitoring
  • 3. Advanced architectures and core concepts of deep learning in smart health
  • 4. Comparative analysis of deep learning frameworks for different disease detection
  • 5. Advantages of deep learning in smart medical healthcare analytics
  • 6. Deep leering applications for disease prediction
  • 7. Deep learning in research and development
  • 8. Future challenges of deep learning in smart health diagnosis and treatment
  • 9. Limitations of deep learning frameworks
  • 10. Conclusion and future scope
  • References
  • 2
  • Fermatean fuzzy approach of diseases diagnosis based on new correlation coefficient operators
  • 1. Introduction
  • 2. Fermatean fuzzy sets and their correlation operators
  • 2.1 Fermatean fuzzy sets
  • 2.2 Existing correlation operators for Fermatean fuzzy sets
  • 3. New Fermatean fuzzy correlation operators
  • 3.1 Computational example
  • 4. Application example of medical diagnosis
  • 5. Conclusion
  • References
  • 3
  • Application of Deep-Q learning in personalized health care Internet of Things ecosystem
  • 1. Introduction
  • 2. Related work
  • 3. Proposed mechanism
  • 4. Experimental results
  • 5. Future directions
  • 6. Conclusion
  • References
  • 4
  • Dia-Glass: a calorie-calculating spectacles for diabetic patients using augmented reality and faster R-CNN
  • 1. Introduction
  • 2. Related works
  • 3. Diabetes: categories, concerns and prevalence
  • 3.1 Categories
  • 3.2 Concerns
  • 3.3 Diabetes prevalence.
  • 4. System methodology
  • 4.1 User information insertion module
  • 4.2 Data acquisition through spectacles
  • 4.3 Food recognition using faster R-CNN
  • 4.4 Calorie estimation
  • 4.5 Information rendering and user notification
  • 5. Result and analysis
  • 5.1 Dataset
  • 5.2 Performance analysis
  • 6. Conclusion
  • References
  • 5
  • Synthetic medical image augmentation: a GAN-based approach for melanoma skin lesion classification with deep le ...
  • 1. Introduction
  • 1.1 Background and motivation
  • 1.2 Contributions
  • 1.3 Related works
  • 2. Skin lesion classification methodology
  • 2.1 Dataset
  • 2.2 CNN architecture with modified VGG16
  • 3. Generation of synthetic skin lesions
  • 3.1 Traditional data augmentation
  • 3.2 Generative adversarial networks for skin lesion synthesis
  • 3.3 Conditional skin lesion synthesis
  • 4. Experimental results
  • 4.1 Dataset evaluation and performance metrics
  • 4.2 Implementation specifications
  • 4.3 Training and test data
  • 5. Performance comparison
  • 6. Conclusion and future work
  • 7. Conflict of interest
  • References
  • 6
  • Artificial intelligence representation model for drug-target interaction with contemporary knowledge and develo ...
  • 1. Introduction
  • 1.1 AI will challenge the status Quo in healthcare
  • 2. AI privacy and security challenges
  • 2.1 Ensuring transparency, explain ability, and intelligibility
  • 2.1.1 Algorithmic fairness and biases
  • 2.1.2 Data availability
  • 2.1.3 Privacy concerns
  • 3. Ensuring transparency, explain ability, and intelligibility
  • 3.1 Data availability
  • 3.2 Concerns regarding privacy
  • 4. Drug discovery and precision medicine with deep learning
  • 4.1 Discrimination and unequal treatment
  • 4.2 The production of data and its availability
  • 4.3 The supervision of quality
  • 5. Clinical decision support and predictive analytics.
  • 5.1 Natural language processing could translate EHR jargon for patients
  • 5.2 Faster drug screening in the future
  • 5.3 Machine learning predictions rely on input data
  • 5.4 Connection between quantifiable construction and function
  • 5.4.1 Support for clinical decision-making and predictive analytics are included in this section
  • 5.4.2 In order to make prescriptive modeling useful, they must be put into practice
  • 6. Natural language processing in drug
  • 7. Predictive analytics has a wide range of practical applications, including the following
  • 7.1 Efforts made to lessen potential dangers to healthcare organizations' security
  • 7.2 Future of deep learning in healthcare
  • 8. Conclusion
  • References
  • 7
  • Review of fog and edge computing-based smart health care system using deep learning approaches
  • 1. Introduction
  • 2. Literature review
  • 3. Healthcare using artificial intelligence
  • 4. Efficient health care system with improved performance
  • 4.1 Dataset
  • 5. Conclusion
  • References
  • 8
  • Deep learning in healthcare: opportunities, threats, and challenges in a green smart environment solution for s ...
  • 1. Introduction
  • 1.1 Major findings and motivation
  • 1.2 The coal crisis creates the need for alternatives [2]
  • 1.3 With minor delays caused by COVID-19, renewable volumes at auction continue to break records [3]
  • 2. Green infrastructure measures in the legislature [4]
  • 2.1 Approaches within the direction of a green economy
  • 2.1.1 An account of the ongoing power situation in Iceland: a global paradigm [5]
  • 2.1.2 Saudi Arabia's Vision 2030 [6,7]
  • 2.1.3 NEOM [8]
  • 2.1.4 Saudi Arabia, Line [9,10]
  • 3. Employment creation as part of the sustainable recovery
  • 3.1 Organic agriculture has the ability to create jobs [12]
  • 3.1.1 Brief explanation
  • 4. Carbon power.
  • 4.1 There are many rehabilitation strategies that have a favorable impact on the environment
  • 4.2 Benefits
  • 5. Smart buildings [15]
  • 6. Climate change disclosure laws [16]
  • 7. The action of biodiversity [17]
  • 7.1 Smart houses and smart buildings: weather [17]
  • 7.1.1 Measures
  • 7.1.2 Benefits
  • 7.1.3 Concise description
  • 8. Case study
  • 8.1 Acceptable approved cases of integrating biodiversity in response to COVID-19 and rehabilitation programs [18]
  • 8.2 Securing Georgia's forest by space [19,20]
  • 8.3 Kazakhstan needs waste management and community well-being technologies [21]
  • 9. Future pandemic preparedness [22]
  • 9.1 We should be concerned about the legal wildlife trade in order to prevent the next pandemic [23]
  • 10. Recent literature
  • 10.1 A comparative analysis of data-driven based optimization models for energy-efficient buildings [24]
  • 10.2 Machine learning forecasting model for the COVID-19 pandemic in India [25]
  • 10.3 AI-based building management and information system with multi-agent topology for an energy-efficient building: toward occu ...
  • 11. Conclusions
  • 11.1 Advantages
  • 11.2 Limitations
  • References
  • Further reading
  • 9
  • Hybrid and automated segmentation algorithm for malignant melanoma using chain codes and active contours
  • 1. Introduction
  • 1.1 Motivation and contribution
  • 1.2 Related works
  • 1.3 Paper organization
  • 2. Materials and methods
  • 2.1 Datasets
  • 2.2 Image enhancement
  • 3. Proposed methodology
  • 3.1 Preprocessing phase
  • 3.2 h-CEAC segmentation
  • 3.2.1 Feature extraction and chain codes
  • 3.2.2 Formulation of the chain code
  • 3.2.3 Pixel interdependence
  • 3.2.4 EDRS and active contours
  • 4. Results and discussions
  • 4.1 Comparison with existing algorithms
  • 5. Conclusion and future scope
  • References.
  • 10
  • Development of a predictive model for classifying colorectal cancer using principal component analysis
  • 1. Introduction
  • 2. Related works
  • 3. Methodology
  • 3.1 Experimental dataset
  • 3.2 Dimensionality reduction tool
  • 3.3 Classification
  • 3.3.1 Support vector machine
  • 3.3.2 K-nearest neighbor
  • 3.3.3 Random forest
  • 3.4 Research tool
  • 3.5 Performance evaluation metrics
  • 4. Results and discussions
  • 5. Conclusion
  • References
  • 11
  • Using deep learning via long-short-term memory model prediction of COVID-19 situation in India
  • 1. Introduction
  • 1.1 Research gaps and motivation
  • 2. Literature review
  • 2.1 Symptoms of COVID-19
  • 3. How to protect yourself from COVID-19
  • 3.1 High-risk groups
  • 4. Facts about the vaccine against the COVID-19
  • 4.1 Vaccines in COVID-19
  • 4.2 Here's a peek at some of the initiatives
  • 4.3 Immunology and antigen detection for the COVID-19 vaccine
  • 4.4 Potential vaccine-related threats
  • 4.5 Vaccines have been approved in India
  • 4.5.1 Covishield
  • 4.5.2 Covaxin
  • 4.5.3 Sputnik V
  • 5. Materials and methods
  • 5.1 Artificial neural network (ANN)
  • 5.2 Model of a neuron
  • 5.2.1 Recurrent neural network (RNN)
  • 6. Results discussion
  • 6.1 Top 10 states (confirmed cases and cured cases in Covid-19)
  • 7. Conclusion
  • References
  • 12
  • Post-COVID-19 Indian healthcare system: Challenges and solutions
  • 1. Creation of robust healthcare system-A nationwide priority and its emergence
  • 2. Pandemonium scenes
  • 3. Efforts for healthcare system development
  • 4. Providing treatment to all amidst difficulties
  • 5. Corona warriors and their woes
  • 6. Transformation of Indian healthcare sector post COVID-19
  • 7. Healthcare component 1-Hospitals
  • 8. Healthcare component 2-Pharmaceutical industry
  • 9. Healthcare component 3-Medical devices and equipment.