Mobile health : advances in research and applications / Gaurav Gupta [and three others].

Format:

Book

Author/Creator:

Gupta, Gaurav, author.

Series:

Health Care in Transition

Language:

English

Subjects (All):

Telecommunication in medicine.

Mobile communication systems.

Medical technology.

Physical Description:

1 online resource (344 pages) : illustrations

Place of Publication:

New York, New York State : Nova Science Publishers, [2021]

Summary:

"Smart health technologies continue to gain research interest across the globe in this digital era. Researchers are focusing on advancements in healthcare systems to make human life better. Also, such advancements help in early disease diagnosis and prevention of the worst diseases. Designing smart healthcare systems is possible only because of recent developments in artificial intelligence, machine learning and IoT technologies. Though mHealth refers to all mobile devices which can communicate data, mobile phones are presently the most popular platform for mHealth delivery. Ninety-four percent of the world population owns/uses a mobile phone, making mobile phones an optimal delivery platform for mHealth interventions. mHealth may catalyse the healthcare delivery model from a historical/episodic model into a tangible/patient-centric model. mHealth is being viewed progressively by many as an essential technology metaphor to achieve rich, vigorous patient engagement, ultimately achieving a patient-centric paradigm change. This book will discuss diverse topics to explain the rapidly emerging and evolving mobile health and artificial perspective, the emergence of integrated platforms and hosted third-party tools, and the development of decentralized applications for various research domains. It presents various applications that are helpful for research scholars and scientists who are working toward identifying and pinpointing the potential of as well as the hindrances to mHealth. The wide variety in topics it presents offers readers multiple perspectives on a variety of disciplines. The aim of this edited book is to publish the latest research advancements in the convergence of automation technology, artificial intelligence, biomedical engineering and health informatics. This will help readers to grasp the extensive point of view and the essence of recent advances in this field. This book solicits contributions which include theory, case studies and computing paradigms pertaining to healthcare applications. The prospective audience would be researchers, professionals, practitioners, and students from academia and industry who work in this field. We hope the chapters presented will inspire future research from both theoretical and practical viewpoints to spur further advances in the field. A brief introduction about each chapter follows. Chapter 1 focuses on the role of Internet of Things (IoT) technologies in healthcare which provides an overview of the various types of IoT devices and data generating equipment for medical information. In Chapter 2, the objective is to provide a brief discussion about the advantages and disadvantages of using IoT based technologies in healthcare such as wearable devices. Chapter 3 deals with important aspects of data science for healthcare systems, which includes various algorithms for decision support system algorithms. Chapter 4 discusses various innovative technologies like digital twins for healthcare and medical diagnosis. Chapter 5 discusses research investigating the long-term effects of pregnancy and lactation on the female body. Chapter 6 summarizes recent advances in machine and deep learning techniques for smart healthcare applications. Chapter 7 explores the research insights on using an artificial neural network with a wrapper-based feature selection to predict heart failure. Chapter 8 presents a review on context-aware mobile healthcare for smart health services in nursing homes. Chapter 9 focuses on certain machine learning methods that can help in early prediction of pandemics. Chapter 10 explores techniques and methods based on machine learning for malaria diagnosis. Chapter 11 is a complete discussion about mobile health technology to improve health-related quality of life of chronic disease patients in emerging economies"-- Provided by publisher.

Contents:

Intro

Contents

Preface

Chapter 1

Role of IoT in Healthcare: An Overview

Abstract

1. Introduction

1.1. What Is the Internet of Things (IoT)?

1.2. Aim of IoT in Healthcare

2. Literature Survey

3. Applications of IoT in Healthcare Field

4. Security Concerns

4.1. Challenges

Conclusion

References

Chapter 2

Wearable Devices: Pros and Cons

Introduction

Literature Survey

Wearable Devices with Their Pros and Cons

AVA Bracelet

AliveCor

TEMPTRAQ

Smart Sleep Wearable

Bio Patch MC100

Chapter 3

Decision Support Algorithms for Data Analysis

1.1. Brief History of Decision Support System

1.2. Characteristics of Decision Support System Network (DSS) (Campion Jr, Waitman et al. 2010

Tripathi 2011)

1.3. Elements of Decision Support System

2. User Involved in Developing a Décision Support System

3. Components Involved in the Développent of a Décision Support System

3.1. Categorization/Classification of DSS

4. Limitation and Disadvantages of Décision Support System

4.1. Limitations of Decision Support Systems (DSS)

4.2. Disadvantages of Decision Support Systems

5. Analyzing A Business Décision Making Process

5.1. Types of Managerial Decisions

6. Développent of a Décision Support System

6.1. Role of Decision Support System in the Management Information System (MIS)

6.2. Designing, Building, and Implementation of an Ideal Decision Support System

6.3. Gaining Competitive Advantage with Decision Support Systems

6.4. Technology Trends in DSS

6.5. Designing and Developing Decision Support Systems

6.6. Choosing a System Development Approach

6.7. User Interface (UI) platform for Designing a Decision Support System (DSS) an Approach

6.7.1. User Interface Styles.

6.7.2. Elements Influencing User Interface Design Success

6.8. Networking and Security Related Issues in DSS Architecture

6.8.1. How Are the DSS Based Architecture, Network, and Security Is Interdependent?

7. Décision Support System Models

7.1. Building a Model-Driven based DSS (MDSS)

7.2. Model Types

7.3. The Following Are Different Sorts of Decision Analysis Processes in DSS Models

7.4. Data Mining and Creating Knowledge

7.4.1. Data Mining Tools and Techniques

7.5. Development of Inter-Organizational Decision Support Systems and web-based interconnected W-DSS

7.5.1. Designing and Developing Web-Based DSS

7.6. Managing Web-Based and Inter-Organizational Decision Support System

8. Machine Learning/Deep Learning-Based Approach, Algorithm and Case Studies for a Décision Support System

8.1. Case Studies Involved Which Uses the Machine Learning and Deep Learning

8.1.1. Case Study 1. DSS and ML Method for the Big Data Mining

8.1.2. Case Study 2: Credit Card Detection, Scoring Based Machine Learning Approach

8.1.3. Case Study 3: Decision Support System In Case of a Health Care System

8.1.4. Case Study 4: Decision Support System in the Field of Sports, Manufacturing

Final Thoughts/Conclusion

Acknowledgments

Chapter 4

Innovation Insight for Healthcare Provider Digital Twins: A Review

1.1. Digital Twins in Healthcare Delivery

1.2. Digital Twin Types in Healthcare Delivery

1.3. Digital Twin Usability Risks

1.4. Digital Twin Process Improvement Cycles

2. Recommendations for Digital Twin Strategy

2.1. Include a Concise Digital Twin Vision within the HDO Digital Transformation Strategy

2.2. Educate Business and Clinical Units About Digital Twins

2.3. Create a Digital Twin Pilot Program

2.4. From Evolution to Revolution.

2.5. Future Perspectives

3. Case Studies

3.1. DT for Heart Care

3.2. DT &amp

Emotions

3.3. DT &amp

Sports

3.4. Digitizing the Human Body Using Virtual Simulations

4. Empowering DT with Blockchain

4.1. Blockchain as a Powerful Antidote

4.1.1. Eliminating Counterfeits

4.2. Combination of Digital Twins and Blockchain

4.2.1. Consider a Premium Watch

4.2.2. Digital Twin and Blockchain in Logistics

4.2.3. Digital Twin and Blockchain in Utilities

4.2.4. Digital Twin and Blockchain in Healthcare

4.2.5. Aircraft Industry

4.3. Usage of IBM Watson &amp

Blockchain Approach to Digital Twins

4.4. Blockchain and Digital Twins for Enhanced Digital Value

Chapter 5

LASIK Innovation Technology for Disease Identification During Lactation

2. Breastfeeding Along with Operation for the LASIK Ogle

3. Basics of LASIK

3.1. Are You a Successful LASIK Contender?

3.2. The Eyes Understanding

3.3. Additional Considerations

3.4. Complications and Threats

3.4.1. Inconvenience

3.5. Why Is LASIK Not Made Up of Women Who Are Pregnant or Breastfeeding?

4. Regarding LASIK Surgery

4.1. Surgery and Breastfeeding in LASIK: The Whole Thing You Should Be Familiar With

4.2. LASIK Pregnancy Before, During and After

4.3. How Your Vision Is Affected by Pregnancy and Breastfeeding?

4.4. LASIK before Pregnancy Surgery

4.5. During Pregnancy, LASIK

4.6. LASIK Following Pregnancy

4.7. Why Pregnant Women Are Not Successful LASIK Candidates

4.8. Why Nursing Women Are Not Successful LASIK Candidates

4.9. Candidacy during Pregnancy and Breastfeeding for LASIK

5. Is It Possible to Take LASIK Surgery for a Pregnant?

5.1. Corporal Eyesight Improves Appropriate to Pregnancy

5.2. Issues of Protection during Pregnancy.

5.3. The Nursing Mother's Considerations

6. Consider Correction of LASER Vision

6.1. Clearer Vision Comfort

6.2. Investment of Wise

6.3. Almost-Instant Results

6.4. Reduction in Signs of Allergy

6.5. Raise the Quotient of Your Style

6.6. Recognizing the LASIK Patient Ideal

7. Issues of Corneal

8. Astigmatism

9. Criteria Exclusion

9.10. Motivation and Comprehension of Patients

9.11. Patients Presbyopic

9.12. LASIK's Medical Alternatives

10. Laser Surgery Drawbacks

10.1. Limitations on Age

10.2. Limitations on Pregnancy or Breastfeeding

10.3. Corneal Thickness Limitations

10.4. Corneal Curvature Limitations

Chapter 6

A Prospective and Comparative Study of Machine and Deep Learning Techniques for Smart Healthcare Applications

Machine and Deep Learning in Medical Diagnosis

Overview of ML and DL Techniques

Supervised Learning

Unsupervised Learning

Reinforcement Learning

Literature on This Area

Alzheimer's Disease

Coronavirus

Glaucoma

Arrhythmia

Diabetes

Result and Discussions

Deep Learning over Machine Learning

Chapter 7

Context-Aware Mobile Healthcare for Smart Health Services in Nursing Homes

1.1. Mobile Healthcare

1.2. Introduction to Mobile Healthcare

1.3. Significance of Mobile Healthcare

2. Digital Healthcare Services

2.1. Various Digital Healthcare Services

2.2. Telehealth and Telemedicine

2.3. Electronic Health Record

2.4. Pharmaceutical Record

2.5. Significance of Digital Healthcare Services

2.6. Limitation of Digital Healthcare Services

3. Digital Healthcare Services Using Mobile Healthcare

3.1. Mobile Healthcare Based Telemedicine

3.2. Mobile Healthcare Based Electronic Health Record.

3.3. Mobile Healthcare Based Pharmaceutical Record

4. Case Study of Mobile Healthcare in a Nursing Home

Chapter 8

Machine Learning Techniques to Fight against Pandemic: A Review

Pandemic Characteristics (Qiu and Rutherford et al., 2016-2017)

Infection Spread

Vast Terrestrial Activity

Lower Resistance Capacity

Unfamilarity

Epidemic vs Pandemic

Part 1

Part 2

Influenza A

Cholera

HIV/AIDS

Zika

Ebola

Various Machine Learning Approaches

Outlook for Future

Chapter 9

Computational Approaches for Malaria Diagnosis Using Machine Learning: A Review

Machine Learning

Machine Learning (ML) in Healthcare

Limitless Opportunities for ML in Healthcare

Malaria Disease

Signs and Symptoms of Malaria

Diagnosis of Malaria

Light Microscopy

Rapid Diagnostic Tests

Other Tests

Fluorescent Microscopy

Flow Cytometry

Staining Methods

Automated Diagnosis of Malaria

Mobile Smartphones for Malaria Diagnosis

Chapter 10

Can Mobile Health Technology Improve Health Related Quality of Life of Chronic Disease Patients in Emerging Economies?: "Happy Heart" A Randomized Controlled Trial in India

1.1. What Is Mobile-Health (m-Health)

1.2. Health-Related Quality of Life (HRQOL)

Overview of m-Health over the Past Decade

2. Methodology

2.1. Development and Pilot Testing of M-Health (Happy Heart) Program

Content Development

Software Development

Expert Assessment of Mobile Application

User Satisfaction Evaluation

Development of SMS Program

2.2. HRQOL Study Tool

2.3. m-Health Study Details.

Statistical Analysis.

Notes:

Description based on print version record.

Includes index.

ISBN:

1-5361-9468-9

OCLC:

1244630688