Deep Learning in Biomedical and Health Informatics: Current Applications and Possibilities
- Length: 224 pages
- Edition: 1
- Language: English
- Publisher: CRC Press
- Publication Date: 2021-09-27
- ISBN-10: 0367726041
- ISBN-13: 9780367726041
- Sales Rank: #0 (See Top 100 Books)
This book provides a proficient guide on the relationship between Artificial Intelligence (AI) and healthcare and how AI is changing all aspects of the healthcare industry. It also covers how deep learning will help in diagnosis and the prediction of disease spread. The editors present a comprehensive review of research applying deep learning in health informatics in the fields of medical imaging, electronic health records, genomics, and sensing, and highlights various challenges in applying deep learning in health care. This book also includes applications and case studies across all areas of AI in healthcare data. The editors also aim to provide new theories, techniques, developments, and applications of deep learning, and to solve emerging problems in healthcare and other domains. This book is intended for computer scientists, biomedical engineers, and healthcare professionals researching and developing deep learning techniques.
In short, the volume :
- Discusses the relationship between AI and healthcare, and how AI is changing the health care industry.
- Considers uses of deep learning in diagnosis and prediction of disease spread.
- Presents a comprehensive review of research applying deep learning in health informatics across multiple fields.
- Highlights challenges in applying deep learning in the field.
- Promotes research in ddeep llearning application in understanding the biomedical process.
Dr.. M.A. Jabbar is a professor and Head of the Department AI&ML, Vardhaman College of Engineering, Hyderabad, Telangana, India.
Prof. (Dr.) Ajith Abraham is the Director of Machine Intelligence Research Labs (MIR Labs), Auburn, Washington, USA.
Dr.. Onur Dogan is an assistant professor at İzmir Bakırçay University, Turkey.
Prof. Dr. Ana Madureira is the Director of The Interdisciplinary Studies Research Center at Instituto Superior de Engenharia do Porto (ISEP), Portugal.
Dr.. Sanju Tiwari is a senior researcher at Universidad Autonoma de Tamaulipas, Mexico.
Cover Half Title Series Page Title Page Copyright Page Table of Contents Tables Figures Abbreviations Preface Acknowledgments Notes on the Editors Contributors Chapter 1: Foundations of Deep Learning and Its Applications to Health Informatics 1.1 Introduction 1.2 History of Deep Learning 1.3 Deep Learning Algorithms 1.4 Applications of DL in Healthcare 1.4.1 Medical Image Processing 1.4.2 Drug Discovery 1.4.3 Protein Structure Analysis 1.4.4 Biomarkers 1.4.5 Bioinformatics 1.4.6 Medicinal Informatics 1.4.7 Public Health 1.5 Case Studies 1.5.1 Deep Learning in Lung Cancer Prediction 1.5.1.1 History of Deep Learning in Lung Cancer Prediction 1.5.2 Deep Learning in Breast Cancer Prediction 1.5.2.1 History of Deep Learning in Breast Cancer Prediction 1.5.3 Deep Learning in Heart Disease Prediction 1.5.3.1 History of Deep Learning in Heart Disease Prediction 1.5.4 Deep Learning in Brain Tumor Prediction 1.5.4.1 History of Deep Learning in Brain Tumor Prediction 1.5.5 Deep Learning in Parkinson’s Disease Prediction 1.5.5.1 History of Deep Learning in Parkinson’s Disease Prediction 1.5.6 Deep Learning in Sleep Apnea Prediction 1.5.6.1 History of Deep Learning in Sleep Apnea Prediction 1.6 Deep Learning Challenges in Health Informatics 1.7 Conclusions References Chapter 2: Deep Knowledge Mining of Complete HIV Genome Sequences in Selected African Cohorts 2.1 Introduction 2.1.1 The HIV-1 Genome Structure 2.1.2 Specific Objectives and Contribution to Knowledge 2.2 Related Works 2.2.1 Characterization of HIV-1 Subtypes in Africa 2.2.2 Application of Machine Learning to HIV Feature Extraction and Prediction 2.3 Methods 2.3.1 Data Collection 2.3.2 Pattern Classification Algorithm 2.3.3 Genome Feature Extraction 2.3.3.1 Nucleotide Transition Frequency 2.3.3.2 Nucleotide Mutation Frequency 2.3.3.3 Cognitive Knowledge Mining 2.3.3.4 Classification Algorithm 2.4 Results 2.4.1 Implementation Workflow 2.4.2 Genome Dataset Transformation 2.4.3 Gene Pattern Visualization 2.4.4 Cognitive Knowledge Extraction 2.4.5 Feature Dataset Extraction 2.4.5.1 Response Cluster Visualization 2.4.6 Target Output Construction 2.4.7 DNN Classification Performance 2.4.8 Findings and Scientific Implication of the Study 2.5 Conclusion References Chapter 3: Review of Machine Learning Approach for Drug Development Process 3.1 Introduction 3.2 Use of Deep Learning/AI 3.3 Drug Development 3.4 Effectiveness of Research and Rate of Developing the Drug 3.5 Drug Development Stages 3.5.1 Drug Discovery 3.5.2 Pre-clinical Studies 3.5.3 Clinical Trials 3.5.4 FDA Analysis 3.5.5 Post-market Review 3.5.5.1 The Society’s Expense 3.6 Use of Machine Learning in Drug Discovery 3.7 To Test the Drug 3.8 Drug Repurposing 3.9 Difficulties Faced by Machine Learning in Drug Discovery 3.10 Discussion 3.11 Conclusion Conflict of Interest Notes References Chapter 4: A Detailed Comparison of Deep Neural Networks for Diagnosis of COVID-19 4.1 Introduction 4.2 Literature Review 4.3 Methodology 4.3.1 Deep Learning Models 4.3.2 Classifiers 4.4 Case Study 4.4.1 Dataset 4.4.2 Preliminaries 4.5 Results and Discussions 4.6 Conclusion and Future Directions Disclosure Statement References Chapter 5: Deep Learning in BioMedical Applications: Detection of Lung Disease with Convolutional Neural Networks 5.1 Introduction 5.2 Lung Diseases 5.3 Convolutional Neural Network (CNN) 5.3.1 Architecture of a Typical Neural Network versus CNN 5.3.1.1 Convolutional Layer 5.3.1.2 Rectified Linear Unit Layer (ReLU) 5.3.1.3 Pooling Layer 5.3.1.4 Fully Connected Layer 5.3.1.5 Softmax Function 5.3.2 Training of CNN 5.4 Lung Disease Diagnosis using CNN 5.4.1 Data Description and Feature Extraction 5.4.2 Applied CNN Architecture 5.4.3 Results 5.5 Conclusion and Evaluation References Chapter 6: Deep Learning Methods for Diagnosis of COVID-19 Using Radiology Images and Genome Sequences: Challenges and Limitations 6.1 Introduction 6.2 Deep Learning Models 6.2.1 Convolutional Neural Networks 6.2.2 Recurrent Neural Networks 6.2.3 Long Short-Term Memory 6.2.4 Bidirectional LSTM 6.2.5 Convolutional LSTM 6.2.6 Gated Recurrent Unit 6.3 Diagnosis of COVID-19 using Genome Sequences 6.4 Diagnosis of COVID-19 using Radiology Images 6.5 Discussion 6.6 Conclusion References Chapter 7: Applications of Lifetime Modeling with Competing Risks in Biomedical Sciences 7.1 Introduction 7.2 Latent Failure Time Approach in Competing Risks 7.3 Bivariate Approach of Competing Risks 7.3.1 Cumulative Incidence Function 7.3.2 Cause Specific Hazard Function 7.3.3 Subdistribution Hazard Function 7.3.4 Mixture Model Approach 7.4 Further Generalization of Bivariate Approach 7.4.1 Direct Parameterization of Cumulative Incidence Function 7.4.2 Full Specified Subdistribution Hazard Model 7.5 Concept of Machine Learning in Survival Analysis 7.5.1 Artificial Neural Networks 7.5.2 Survival Trees 7.5.3 Bayesian Methods of Machine Learning 7.6 Application to Breast Cancer Data 7.7 Discussion and Conclusion References Chapter 8: PeNLP Parser: An Extraction and Visualization Tool for Precise Maternal, Neonatal and Child Healthcare Geo-locations from Unstructured Data 8.1 Introduction 8.1.1 Location Information Mining 8.1.2 Objectives and Contributions to Knowledge 8.2 Related Works 8.2.1 Natural Language Processing of Clinical Data 8.2.2 Spatial Knowledge Extraction from Clinical Data 8.2.3 The Linguistic and Logical Theory of Place 8.3 Materials and Methods 8.3.1 Data Source 8.3.2 The PeNLP Parser Architecture 8.3.3 The PeNLP Parser 8.3.3.1 Sentence Construction 8.3.3.2 Sentence Boundary Detection 8.3.3.3 Tokenization 8.3.3.4 Lexical Matching/Preposition Identification 8.3.3.5 Place Term Extraction (Corpus Generation) 8.3.3.6 Geolocation Identification / Visualization (Linkage to Google Maps) 8.4 Results and Discussion 8.4.1 Input 8.4.2 PeNLP Parser Outputs 8.4.3 Performance Evaluation 8.5 Conclusion and Future Research Perspective Acknowledgements Disclosure Statement Data Availability Statement Declaration of Interest References Chapter 9: Recent Trends in Deep Learning, Challenges and Opportunities 9.1 Introduction 9.2 Deep Learning 9.3 Deep Learning Network 9.4 Deep Learning Neural Network 9.5 Applications of Deep Learning 9.6 Conclusion References Index
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