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Full Description
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.
Contents
1. Foundations of Deep Learning and its Applications to Health Informatics.
2. Deep Knowledge Mining of Complete HIV Genome Sequences in Selected African Cohorts.
3. Review of Machine Learning Approach for Drug development Process.
4. A Detailed Comparison of Deep Neural Networks for Diagnosis of COVID-19.
5. Deep Learning in BioMedical Applications: Detection of Lung Disease with Convolutional Neural Networks
6. Deep Learning Methods For Diagnosis Of Covid-19 using Radiology Images And Genome Sequences: Challenges And Limitations.
7. Applications of Lifetime Modeling with Competing Risks in Biomedical Sciences.
8. PeNLP Parser: An Extraction and Visualization Tool for Precise Maternal, Neonatal and Child Healthcare Geo-locations from Unstructured Data.
9. Recent Trends in Deep learning, Challenges and Opportunities
