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Book Title

Artificial Intelligence and Big Data in Resources Poor Healthcare Systems

                                                                          Trends, Perspectives, and Application

Description

Artificial Intelligence (AI) and Big Data (BD) are the most useful technologies that could powerfully improve health care service delivery, especially in resource-poor countries (Resources Poor Healthcare Systems). Big Data basically consists of big amount of data coming from diverse sources in different formats, while AI includes machine learning (ML) and deep learning (DL) and can be used for repetitive tasks like diseases diagnosis, diseases screening, diseases prevention, early diseases detection, medical decision making, medical treatment using AI-based tools, nursing care at the end of life, home health and medical care, remote artificial doctor, etc.

Many countries, for example, China, are investing in AI for Healthcare to overcome challenges such as healthcare workforce shortage.

Doctors are slowly being assisted by a software system in their daily duties, for example in decision making. Though this can face some limitations. Most AI-based prediction tasks are done using DL technics/technology. However, DL applications are limited in explanatory capacity. A trained DL system cannot explain the “how” of performed predictions – even when the prediction outcomes are correct. This kind of “black box problem” is challenging, especially in healthcare, where doctors don't want to make life-and-death decisions without a firm understanding of how the machine arrived at its recommendation (even if those recommendations have proven to be correct in the past). Since Data sciences have great potential, combining Data sciences (including Data analytics) with AI will be beneficial for Healthcare one on hand and other help to solve the black box issues generated by DL. Data science combines different algorithms, technologies, and systems and assists to extract the right information from well designed and collected data.

This book is looking to explore the concepts of AI (including machine learning, deep learning) and big data (including IoT and Smart City) in health care along with the recent research development with a focus on health care systems resources poor countries (Resources Poor Healthcare Systems) as well as in rural areas in developed countries. It would also include various real-time/offline applications and case studies in the field of engineering, computer science, IoT, Smart Cities with modern tools & technologies used in healthcare.

As a population grows and resources become scarcer, the efficient usage of these limited goods becomes more important. Smart cities are a key factor in the consumption of materials and resources. Built on and integrating with big data, the cities of the future are becoming a realization today. The integration of big data and interconnected technology along with the increasing population will lead to the necessary creation of smart cities. To continue providing people with safe, comfortable, and affordable places to live, cities must incorporate techniques and technologies to bring them into the future. We are looking forward to seeing the advances that will come to our cities soon.

The main objectives of this book are to analyze the chances of effectively using AI in healthcare, especially in resource-poor countries (Resources Poor Healthcare Systems). Furthermore, this book aims at presenting some use cases of AI in healthcare and though show its potential to improve care services delivery, overcome the forthcoming challenges, which will face the sector regarding the increasing world population, as well as resource scarcity and workforce shortage.

The reader will be provided with insight into AI, Big data, and Data sciences for healthcare, the state-of-the-art, chances, challenges, limitations, and perspectives, as well as future development. This book would cover disciplines such as, but not limited to, Artificial Intelligence and Big Data in Resources Poor Healthcare Systems: Theories, Chances, Trends, Perspectives, Future Development, and Applications is a fascinating addition to the literature in this discipline.

Keywords

• Expert system
• Machine learning
• Deep learning
• Drugs discovery
• Big data analytic
• Healthcare technology
• Explainable artificial intelligence 

Unique features of the book

• Explores the concepts of AI (including machine learning, deep learning) and big data (including the Internet of Things and Smart City) in healthcare.
• Includes various real-time/ offline applications and case studies in the field of engineering, computer science, IoT, Smart Cities with modern tools & technologies used in healthcare.
• Provides guidance on how health technology can face the challenge of improving the quality of life regardless of social and financial consideration, gender, age, and residence place.

Submission Guidelines

All papers must be original and not simultaneously submitted to another journal or conference. The following paper categories are welcome (but not limited to):

• Technical Design Papers
• Scientific Evaluation Papers/Systematic Review
• Case reports
• Analysis
• Vision Papers

Chapter proposal submission: December 15^th, 2019 (max. 500 words)

Acceptance notification: December 20^th, 2019

Full chapters submission: April 30^th, 2020 (max. 5 tables/figures, 25 pages, 15.000 to 20.000 words)

Full chapter acceptance notification: July 2^nd, 2020

Final and revised chapters due to: August 30^th, 2020

Template and SpringerNature format

Please use the Springer Nature format. For detail click here.

List of Topics

• Health informatics/Medical IT
• Health care Services Delivery and management
• Assistive Decision Making
• Data science
• Artificial intelligence

Tentative Chapters

1. Introduction to AI and Big data for resource-poor Healthcare
2. Introduction to Deep learning for resource-poor healthcare
3. Introduction to smart healthcare in the context of big data and AI
4. Challenges facing AI for Healthcare resource-poor Healthcare
5. AI and Big Data in Healthcare for resource-poor Healthcare: Trends and perspectives
6. Convolutional neural network for improving resource-poor Healthcare
7. Deep believe network for medical and health applications in resource-poor Healthcare
8. Deep learning network architectures for medical and health applications to improve resource-poor Healthcare.
9. Stack Autoencoder for multimodal health care to improve resource-poor Healthcare 
10. Deep learning scalability models in improving resource-poor Healthcare
11. Expert system for improving resource-poor Healthcare
12. Expert systems in smart hospitals and clinics
13. Developing smart doctors based on explainable AI for resource-poor Healthcare
14. Explainable AI-based Remote Care to improve resource-poor Healthcare
15. Explainable AI-based Medical Data Exchanges and Data interoperability for resource-poor Healthcare
16. Explainable AI-based Electronic Health Records for resource-poor Healthcare
17. Improving resource-poor Healthcare based on Explainable AI
18. Expert system for exploring protein secondary structure to improve resource-poor Healthcare
19. Exploiting structure in information for resource-poor Healthcare
20. Making use of heterogeneous information in machine learning for resource-poor Healthcare.
21. Monitoring to personalized medicine through expert systems for resource-poor Healthcare.
22. Improving patient privacy and security through expert systems for resource-poor Healthcare
23. Deep learning in drug discovery for resource-poor Healthcare
24. Deep Transfer Learning in protein structure for resource-poor Healthcare
25. Hybrid of nature-inspired algorithm and deep learning structure for improving resource-poor Healthcare in developing countries
26. Case studies of AI and big data in Healthcare delivery in improving resource-poor Healthcare

Editorial Advisory Board Members

1. Samuel Fosso-Wamba, Toulouse Business School, France
2. Haruna Chiroma, Federal College of Education, Gombe, Nigeria
3. Jules Degila, IMSP, Université d’Abomey-Calavi, Benin
4. Pravin Pawar, SUNY, Korea.

Editors

1. Thierry Edoh, RFW-Universität Bonn, Bonn Germany
2. Vijayalakshmi Kakulapati, Sreenidhi Institute of Science and Technology, Yamnampet, Ghatkesar, Hyderabad India

Publication

This book will be published in Series Health Informatics (https://www.springer.com/series/1114)

by Springer Nature Switzerland AG. The address for which is: Gewerbestrasse 11, 6330 Cham, Switzerland Copyright Holder: Springer Nature Switzerland AG

Contact

All questions about submissions should be emailed to  Thierry Edoh (oscar.edoh@gmail.com)