Deep Learning in Personalized Healthcare and Decision Support
- 10h 50m
- Harish Garg, Jyotir Moy Chatterjee
- Elsevier Science and Technology Books, Inc.
- 2023
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, clinical decision support system, clinical trials, electronic health records, precision medication, Parkinson disease detection, genomics, and drug discovery. In addition, it discusses the use of DL for fraud detection and internet of things. This is a valuable resource for researchers, graduate students and healthcare professionals who are interested in learning more about deep learning applied to the healthcare sector.
Although there is an increasing interest by clinicians and healthcare workers, they still lack enough knowledge to efficiently choose and make use of technologies currently available. This book fills that knowledge gap by bringing together experts from technology and clinical fields to cover the topics in depth.
- Discusses the application of deep learning in several areas of healthcare, including clinical trials, telemedicine and health records management
- Brings together experts in the intersection of deep learning, medicine, healthcare and programming to cover topics in an interdisciplinary way
- Uncovers the stakes and possibilities involved in realizing personalized healthcare services through efficient and effective deep learning technologies
About the Author
Dr. Garg is one of the pioneer researchers in the world. He ranks in the World's Top 2% Scientist List and Rank #1 in India & World Rank #229 published by Stanford University in the consecutive three years 2020, 2021, and 2022. He is the recipient of the Obada-Prize 2022 – Young Distinguished Researchers. He is also the recipient of the Top-Cited paper by an India-based author (2015 – 2019) from Elsevier Publisher. He also serves as an advisory board member of the Universal Scientific Education and Research Network (USERN). Dr. Garg's research interests include Computational Intelligence, Multi-criteria decision making, Evolutionary algorithms, Reliability analysis, Expert systems, and decision support systems, Computing with words, and Soft Computing. He has authored more than 400 papers (over 350 are SCI) published in refereed International Journals including IEEE Transactions, Elsevier, Springer, etc. He has also authored seven book chapters. Also, he edited 8 books from Elsevier, Springer, and other publishers. His Google citations are over 17650 with an H-index of- 75. Dr. Garg also serves on editorial boards of several leading international journals, this includes the Founding Editor-in-Chief of the Journal of Computational and Cognitive Engineering. He is also the Associate Editor of IEEE Transaction of Fuzzy Systems, Soft Computing, Alexandria Engineering Journal, Journal of Intelligent & Fuzzy Systems, Complex and Intelligent Systems, Journal of Industrial & Management Optimization, CAAI Transactions on Intelligence Technology, etc. Dr. Garg also shared his knowledge (Teaching as well as Research) with the whole world through their YouTube channel https://www.youtube.com/c/DrHarishGarg. For more details about him, kindly follow his webpage https://sites.google.com/site/harishg58iitr/home
Jyotir Moy Chatterjee received M. Tech in Computer Science & Engineering from Kalinga Institute of Industrial Technology (KIIT), Bhubaneswar, Odisha in 2016, and a B. Tech in Computer Science & Engineering from Dr. MGR Educational & Research Institute, Maduravoyal, Chennai in 2013. His research interests include Machine Learning, Deep Learning, and the Internet of Things. He is credited with 26 SCIE journal papers, 9 Scopus journal papers, 43 Google Scholar journal papers, 5 international conference papers, 3 authored books, 21 edited books, 26 book chapters, and 1 Indian patent (published). He is currently serving as a Commissioning Editor of the Journal of Information Technology Research (JITR- IGI Global) and Academic Editor of the Computational Intelligence and Neuroscience (Hindawi). He is also serving as a reviewer for various prestigious Elsevier, Springer, IEEE, and Techpress journals and is also associated with various international conferences as a reviewer, TPC, Advisory board, etc. He is also the book series editor of the Scrivener Publishing- MACHINE LEARNING IN BIOMEDICAL SCIENCE AND HEALTHCARE INFORMATICS (https://www.scrivenerpublishing.com/series.php?id=Machine%20Learning%20in%20Biomedical%20Science%20and%20Healthcare%20Informatics), CRC Press- Chapman & Hall/CRC Cyber-Physical Systems (https://www.routledge.com/Chapman--HallCRC-Cyber-Physical-Systems/book-series/CHCPS), CRC Press- Artificial Intelligence in Smart Healthcare Systems(https://www.routledge.com/Artificial-Intelligence-in-Smart-Healthcare-Systems/book-series/CRCAISHS?publishedFilter=alltitles&pd=published,forthcoming&pg=1&pp=48&so=pub&view=list). For more details about him, kindly follow his webpage https://sites.google.com/view/jyotirmoychatterjee
In this Book
-
The Future of Health Diagnosis and Treatment: An Exploration of Deep Learning Frameworks and Innovative Applications
-
Fermatean Fuzzy Approach of Diseases Diagnosis Based on New Correlation Coefficient Operators
-
Application of Deep-Q Learning in Personalized Health Care Internet of Things Ecosystem
-
Dia-Glass: A Calorie-Calculating Spectacles for Diabetic Patients Using Augmented Reality and Faster R-CNN
-
Synthetic Medical Image Augmentation: A GAN-Based Approach for Melanoma Skin Lesion Classification with Deep Learning
-
Artificial Intelligence Representation Model for Drug–Target Interaction with Contemporary Knowledge and Development
-
Review of Fog and Edge Computing–Based Smart Health Care System Using Deep Learning Approaches
-
Deep Learning in Healthcare: Opportunities, Threats, and Challenges in a Green Smart Environment Solution for Smart Buildings and Green Cities—Towards Combating COVID-19
-
Hybrid and Automated Segmentation Algorithm for Malignant Melanoma Using Chain Codes and Active Contours
-
Development of a Predictive Model for Classifying Colorectal Cancer Using Principal Component Analysis
-
Using Deep Learning via Long-Short-Term Memory Model Prediction of COVID-19 Situation in India
-
Post-COVID-19 Indian Healthcare System: Challenges and Solutions
-
SWOT Perspective of the Internet of Healthcare Things
-
Deep Learning for Clinical Decision-Making and Improved Healthcare Outcome
-
Development of a No-Regret Deep Learning Framework for Efficient Clinical Decision-Making
-
Symptom-Based Diagnosis of Diseases for Primary Health Check-Ups Using Biomedical Text Mining
-
“Deep Learning” for Healthcare: Opportunities, Threats, and Challenges
-
Deep Learning IoT in Medical and Healthcare
-
Deep Learning in Drug Discovery
-
Avant-Garde Techniques in Machine for Detecting Financial Fraud in Healthcare
-
Predicting Mental Health Using Social Media: A Roadmap for Future Development
-
Applied Picture Fuzzy Sets with Its Picture Fuzzy Database for Identification of Patients in a Hospital
-
A Deep Learning Framework for Surgery Action Detection
-
Understanding of Healthcare Problems and Solutions Using Deep Learning
-
Deep Convolution Classification Model-Based COVID-19 Chest CT Image Classification
-
Internet of Medical Things in Curbing Pandemics
