Healthcare 4.0: Health Informatics and Precision Data Management

298 Pages·2022·36.823 MB·English

by  Lalitha Krishnasamy, Rajesh Kumar Dhanaraj, Balamurugan Balusamy, Munish Sabharwal, Poongodi Chinnasamy

Healthcare 4.0
The main aim of Healthcare 4.0: Health Informatics and Precision Data Management is to
improve the services given by the healthcare industry and to bring meaningful patient
outcomes by applying the data, information and knowledge in the healthcare domain.
Features:
• Improves the quality of health data of a patient
• Presents a wide range of opportunities and renewed possibilities for healthcare
systems
• Gives a way for carefully and meticulously tracking the provenance of medical
records
• Accelerates the process of disease-oriented data and medical data arbitration
• Brings meaningful patient health outcomes
• Eradicates delayed clinical communications
• Helps the research intellectuals to step down further toward the disease and
clinical data storage
• Creates more patient-centered services
The precise focus of this handbook is on the potential applications and use of data
informatics in healthcare, including clinical trials, tailored ailment data, patient and ail-
ment record characterization and health records management.

Healthcare 4.0
Health Informatics and Precision
Data Management
Edited by
Lalitha Krishnasamy
Rajesh Kumar Dhanaraj
Balamurugan Balusamy
Munish Sabharwal
Poongodi Chinnasamy
First edition published 2023
by CRC Press
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© 2023 selection and editorial matter, Lalitha Krishnasamy, Rajesh Kumar Dhanaraj, Balamurugan Balusamy,
Munish Sabharwal and Poongodi Chinnasamy; individual chapters, the contributors
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ISBN: 978-1-032-10860-5 (hbk)
ISBN: 978-1-032-42262-6 (pbk)
ISBN: 978-1-003-21743-5 (ebk)
DOI: 10.1201/9781003217435
Typeset in Palatino
by SPi Technologies India Pvt Ltd (Straive)
Contents
Preface ............................................................................................................................................vii
Editor Biography ............................................................................................................................xi
List of Contributors .....................................................................................................................xiii
1. Privacy-preserving Healthcare Informatics Using Federated
Learning and Blockchain .......................................................................................................1
K. Tamil Selvi and R. Thamilselvan
2. Applications, Opportunities, and Current Challenges in the
Healthcare Industry ..............................................................................................................27
A. Veena and S. Gowrishankar
3. Harnessing Big Data and Artificial Intelligence for Data Acquisition,
Storage, and Retrieval of Healthcare Informatics in Precision Medicine .................51
S. Mohana Saranya, K. Tamilselvi, and S. Mohanapriya
4. Analogous Healthcare Product Identification in Online Shopping ...........................77
N. Archana, R. Menaka, S. M. J. Blessy Regina, and P. M. Lakshmi Prabha
5. Segmentation-based Comparative Analysis for Detection of Bone
Tumour Using Healthcare Data ..........................................................................................97
J. Eric Clapten, A. Tamilselvi, K. Oviya, and M. Swetha
6. Challenges, Progress and Opportunities of Blockchain in Healthcare Data ..............111
Dinesh Komarasamy, M. K. Dharani, R. Thamilselvan, and J. Jenita Hermina
7. SepSense: A Novel Sepsis Detection System Using Machine Learning
Techniques ...........................................................................................................................131
V. Aruna Devi, Sakthi Jaya Sundar Rajasekar, and Varalakshmi Perumal
8. Oral Cancer Detection at Early Stage Using Convolutional Neural
Network in Healthcare Informatics ................................................................................151
S. Bhuvaneswari, R. Pandimeena, M. Sridhar, and S. Vignesh
9. Lung Diseases Identification ............................................................................................173
A. Sivabalan, Jai Jaganath Babu Jayachandran, D. Sandhiya, and M. B. Sharada
10. Brain–Computer Interface-based Real-Time Movement of Upper
Limb Prostheses ..................................................................................................................185
K. Kalpana, B. Hakkem, J. Teresa Dhanasekar, and S. Ramya
v
vi Contents
11. A Robust Image-Driven CNN Algorithm to Detect Skin Disease in
Healthcare Systems ............................................................................................................207
S. Suganyadevi, V. Seethalakshmi, N. Vidhya, and K. Balasamy
12. Patient Identity Ailments and Maintenance Using Blockchain and
Health Informatics ..............................................................................................................229
K. S. Suganya, R. Nedunchezian, and K. S. Arvind
13. An Innovative Outcome of Internet of Things and Artificial Intelligence
in Remote Centered Healthcare Application Schemes ...............................................245
Lalitha Krishnasamy, A. Tamilselvi, and Rajesh Kumar Dhanaraj
14. Electronic Health Records Storing and Sharing System Using Blockchain ...........267
Shailendra S. Aote, Amit Khaparde, Balamurugan Balusamy, Aayush Muley,
Adesh Kotgirwar, Atharva Uplanchiwar, and Lalita Sharma
Index ......................................................................................................................................279
Preface
Health records in the electronic format are increasing day by day and to analyze these
massive records with heterogeneous data sets, informatics and data analysis are obliga-
tory. This book reveals an enigmatic assembly of diverse biomedical data types which
comprise clinical data, data from electrocardiogram, genomic data, etc. Biomedical data
are notorious for their diversified scales, dimensions, and volumes and requires interdisci-
plinary technologies for visual illustration and digital characterization. Various computer
programs and servers have been developed for these purposes by both theoreticians and
engineers. The healthcare industry today is notorious to feel pain from heterogeneous and
uneven data, delayed clinical communications, and disparate workflow tools due to the
lack of identification of proper disease, lack of interoperability caused by vendor-locked
healthcare systems, and security/privacy concerns regarding data storage, sharing, and
usage.
Apart from data informatics, which is of paramount importance for improving quality
of health data of a patient, there is also a wide range of opportunities and renewed possi-
bilities for healthcare systems to influence the categories of health data. This powerful and
breakthrough information system paves a way for carefully and meticulously tracking the
provenance of medical records, accelerating the process of disease-oriented data and medi-
cal data arbitration, connecting alike patient populations to clinical trials, and creating
more patient-centered services.
The precise focus of this handbook will be on the potential applications and use of data
informatics in area of healthcare, including clinical trials, tailored ailment data, patient and
ailment record characterization, and health records management.
Organization of this Book
Chapter 1 presents the big data era; the healthcare informatics need exploration of the
health records to identify the hidden patterns. Machine learning and deep learning tech-
niques provide classification, clustering, and prediction tasks. Healthcare data processed
in the centralized architecture pose single point of failure and it is difficult to collaborate
with different distribution of data to design a robust system. This chapter identifies the
current challenges in the healthcare informatics and addresses those issues with enabling
technologies that explore the healthcare informatics with application of artificial learning
and security mechanisms.
Chapter 2 investigates the state-of-the-art research solutions for the complete range of
healthcare data, analyzes the data, and provides guidance for future progress in healthcare
practice. It highlights the developments in using blockchain technology in healthcare
domain, emphasizing the need for healthcare data to be shared among various healthcare
entities. It then summarizes modern and emerging technologies such as cloud and fog
vii
viii Preface
computing in healthcare industry for classifying illness and segmentation, with an empha-
sis on deep learning and AI and machine learning architectures that have already been
established as the standard methodology.
Chapter 3 presents the recent advances in computer storage systems, processing power,
and data-analysis algorithms in the modern Clinical Information Systems that made the
above task easier. Bioinformatics addresses these problems and aims to create integrated
storage systems and analysis tools to convert the biological data into meaningful informa-
tion. This helps to predict the disease and paves the way for drug discovery in precision
medicine.
Chapter 4 discusses how to purchase medicines online that are rarely available in the
market. In the COVID period, people suffered a lot to get the recommended medicine from
an authorized agency through online. Many fake images were posted and patients received
different medicines than expected. Clinical information system analysis is explored with
image processing techniques to predict the disease and the correct drug identification and
discovery.
Chapter 5 focuses primarily on two strategies for using MATLAB software simulations
to segment bone tissues and then adopting the most effective segmentation approach
based on iImage pre-processing, edge and boundary detection, and other related tasks.
The graph cut technique, which is known for its smoothness and energy economy, has
been more popular in the image processing and analysis disciplines in recent years.
Chapter 6 presents that the hospital has planned to maintain the patient history in the
form of electronic data. The patients do not prefer to maintain the patient history in the
distributed network due to security concerns. Therefore, blockchain technique is evolved
to securely maintain the patient’s history in the distributed networks.
Chapter 7 briefly introduces sepsis, a life-threatening emergency medical condition and
is one of the leading causes of death in patients who are being treated at the intensive care
unit (ICU) of hospitals. It is the extreme response shown by the body in response to infec-
tion. Sepsis arises due to the excessive quantity of infective microbes in the blood which
are in a state of active replication. Here, a novel sepsis detection system using machine
learning techniques has been presented. The system would predict if the patient would
develop sepsis using a range of clinical parameters like blood pressure, temperature, oxy-
gen saturation, heart rate, and other biochemical investigations.
Chapter 8 discusses oral sickness. Two-thirds of the population has been suffering from
oral sickness in Asia for the past three decades, and the ailment is particularly prevalent in
low- and middle-income nations, according to the World Health Organization. A neural
network-based oral cancer detection system that makes use of a range of image processing
methodologies is presented here.
Chapter 9 explores deep learning and is used to identify lungs illness. According to the
Globe Health Organization, lung illnesses would be the third most important cause of
demise in the world by 2030. Lung infection is a common occurrence all around the globe.
The foundation of lung infection diagnosis is the ease with which it may be accomplished.
Chapter 10 proposes that brain–computer interfaces (BCI) circumvent the body’s nor-
mal neuromuscular controls and are intended to act as a backup mechanism for com-
munication and control in the case of a neuronal or muscle failure. The chapter discusses
the effects of BCI on cognition, sensors, machine learning, neurophysiology, psychology,
signal detection and processing, source localization, pattern recognition, clustering, and
classification of signals, as well as signal detection and processing in general.
Chapter 11 highlights segmentation and feature extractions to diagnose the skin condi-
tion at an early stage. Text-based features are extracted with the help of CNN and an image
Preface ix
processing technique that incorporates symmetry detection, border detection, and color. A
major development in healthcare 4.0 is also occurring in the treatment of aging skin.
Chapter 12 presents the technology and or approach utilized to precisely identify
patients, thorough design of the care processes using blockchain will ensure effective
patient identification prior to any medical intervention and result in safer care with fewer
errors. In healthcare, patient information is shared with healthcare experts using electronic
health record (EHR) where patient’s information is shared among the healthcare experts
with the help of patient/healthcare expert’s identity.
Chapter 13 discusses that IoT is a significant improvement in intelligence to the current
societal issues. There is no way to classify the subjective pain associated with such an
active communication network as something that is outside of artificial intelligence, and
we are certainly not outside of artificial intelligence. It is possible to analyze, combine, and
prioritize the data collected in this way if it is required. When physicians work with algo-
rithms, they may change therapy while also delivering more cost-effective healthcare,
which has a more positive result for patients.
Chapter 14 focuses on developing a secure and privacy-preserving health chain system
based on blockchain technology. The system ensures data security and patient privacy by
giving full authority of the data to the patient. It also regulates that the data are updated by
the skilled healthcare professional, only with the permission of the patient. It will automate
the process throughout the chain and contribute toward robustness of the healthcare infor-
mation sharing environment.