Big Data Analytics and Machine Intelligence in Biomedical and Health Informatics E-Book
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- Herausgeber: John Wiley & Sons
- Kategorie: Wissenschaft und neue Technologien
- Serie: Advances in Intelligent and Scientific Computing
- Sprache: Englisch
BIG DATA ANALYTICS AND MACHINE INTELLIGENCE IN BIOMEDICAL AND HEALTH INFORMATICS
Provides coverage of developments and state-of-the-art methods in the broad and diversified data analytics field and applicable areas such as big data analytics, data mining, and machine intelligence in biomedical and health informatics.
The novel applications of Big Data Analytics and machine intelligence in the biomedical and healthcare sector is an emerging field comprising computer science, medicine, biology, natural environmental engineering, and pattern recognition. Biomedical and health informatics is a new era that brings tremendous opportunities and challenges due to the plentifully available biomedical data and the aim is to ensure high-quality and efficient healthcare by analyzing the data.
The 12 chapters in??Big Data Analytics and Machine Intelligence in Biomedical and Health Informatics??cover the latest advances and developments in health informatics, data mining, machine learning, and artificial intelligence. They have been organized with respect to the similarity of topics addressed, ranging from issues pertaining to the Internet of Things (IoT) for biomedical engineering and health informatics, computational intelligence for medical data processing, and Internet of Medical Things??(IoMT).
New researchers and practitioners working in the field will benefit from reading the book as they can quickly ascertain the best performing methods and compare the different approaches.
Audience
Researchers and practitioners working in the fields of biomedicine, health informatics, big data analytics, Internet of Things, and machine learning.
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Seitenzahl: 510
Veröffentlichungsjahr: 2022
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Table of Contents
Cover
Title Page
Copyright
Preface
1 An Introduction to Big Data Analytics Techniques in Healthcare
1.1 Introduction
1.2 Big Data in Healthcare
1.3 Areas of Big Data Analytics in Medicine
1.4 Healthcare as a Big Data Repository
1.5 Applications of Healthcare Big Data
1.6 Challenges in Big Data Analytics
1.7 Big Data Privacy and Security
1.8 Conclusion
1.9 Future Work
References
2 Identify Determinants of Infant and Child Mortality Based Using Machine Learning: Case Study on Ethiopia
2.1 Introduction
2.2 Literature Review
2.3 Methodology and Data Source
2.4 Implementation and Results
2.5 Conclusion
References
3 Pre-Trained CNN Models in Early Alzheimer’s Prediction Using Post-Processed MRI
3.1 Introduction
3.2 Experimental Study
3.3 Data Exploration
3.4 OASIS Dataset Pre-Processing
3.5 Alzheimer’s 4-Class-MRI Features Extraction
3.6 Alzheimer 4-Class MRI Image Dataset
3.7 RMSProp (Root Mean Square Propagation)
3.8 Activation Function
3.9 Batch Normalization
3.10 Dropout
3.11 Result—I
3.12 Conclusion and Future Work
Acknowledgement
References
4 Robust Segmentation Algorithms for Retinal Blood Vessels, Optic Disc, and Optic Cup of Retinal Images in Medical Imaging
4.1 Introduction
4.2 Basics of Proposed Methods
4.3 Experimental Results and Discussion
4.4 Conclusion
References
5 Analysis of Healthcare Systems Using Computational Approaches
5.1 Introduction
5.2 AI & ML Analysis in Health Systems
5.3 Healthcare Intellectual Approaches
5.4 Precision Approaches to Medicine
5.5 Methodology of AI, ML With Healthcare Examples
5.6 Big Analytic Data Tools
5.7 Discussion
5.8 Conclusion
References
6 Expert Systems in Behavioral and Mental Healthcare: Applications of AI in Decision-Making and Consultancy
6.1 Introduction
6.2 AI Methods
6.3 Turing Test
6.4 Barriers to Technologies
6.5 Advantages of AI for Behavioral & Mental Healthcare
6.6 Enhanced Self-Care & Access to Care
6.7 Other Considerations
6.8 Expert Systems in Mental & Behavioral Healthcare
6.9 Dynamical Approaches to Clinical AI and Expert Systems
6.10 Conclusion
6.11 Future Prospects
References
7 A Mathematical-Based Epidemic Model to Prevent and Control Outbreak of Corona Virus 2019 (COVID-19)
7.1 Introduction
7.2 Related Work
7.3 Proposed Frameworks
7.4 Results and Discussion
7.5 Conclusion
References
8 An Access Authorization Mechanism for Electronic Health Records of Blockchain to Sheathe Fragile Information
8.1 Introduction
8.2 Related Work
8.3 Need for Blockchain in Healthcare
8.4 Proposed Frameworks
8.5 Use Cases
8.6 Discussions
8.7 Challenges and Limitations
8.8 Future Work
8.9 Conclusion
References
9 An Epidemic Graph’s Modeling Application to the COVID-19 Outbreak
9.1 Introduction
9.2 Related Work
9.3 Theoretical Approaches
9.4 Frameworks
9.5 Evaluation of COVID-19 Outbreak
9.6 Conclusions and Future Works
References
10 Big Data and Data Mining in e-Health: Legal Issues and Challenges
10.1 Introduction
10.2 Big Data and Data Mining in e-Health
10.3 Big Data and e-Health in India
10.4 Legal Issues Arising Out of Big Data and Data Mining in e-Health
10.5 Big Data and Issues of Privacy in e-Health
10.6 Conclusion and Suggestions
References
11 Basic Scientific and Clinical Applications
11.1 Introduction
11.2 Case Study-1: Continual Learning Using ML for Clinical Applications
11.3 Case Study-2
11.4 Case Study-3: ML Will Improve the RadiologyPatient Experience
11.5 Case Study-4: Medical Imaging AI with Transition from Academic Research to Commercialization
11.6 Case Study-5: ML will Benefit All Medical Imaging ‘ologies’
11.7 Case Study-6: Health Providers will Leverage Data Hubs to Unlock the Value of Their Data
11.8 Conclusion
References
12 Healthcare Branding Through Service Quality
12.1 Introduction to Healthcare
12.2 Quality in Healthcare
12.3 Service Quality
12.4 Conclusion and Road Ahead
References
Index
End User License Agreement
List of Illustrations
Chapter 1
Figure 1.1 Big data in healthcare.
Figure 1.2 Five vs of big data.
Figure 1.3 Areas of big data analytics in medicine.
Chapter 2
Figure 2.1 Overall of missing values.
Figure 2.2 Boruta features importance result.
Figure 2.3 Top twenty features ranked by random forest.
Figure 2.4 Class distribution before applying sampling techniques.
Figure 2.5 C5.0 rule-based decision tree.
Chapter 3
Figure 3.1 Human brain and neurons.
Figure 3.2 Brain atrophy variations of T1 (longitudinal relaxation time), (a) at...
Figure 3.3 Extracting volume of brain by image processing.
Figure 3.4 Structural MRI images representing different AD stages. (a) Mild deme...
Figure 3.5 Age vs demented and non-demented.
Figure 3.6 MMSE.
Figure 3.7 nWBV.
Figure 3.8 eTIV.
Figure 3.9 ASF.
Figure 3.10 AGE.
Figure 3.11 EDUC.
Figure 3.12 Non demented, demented and converted demented MRI count.
Figure 3.13 Polynomial regression of SES and EDUC.
Figure 3.14 Features to be used.
Figure 3.15 Ensemble learning.
Figure 3.16 Random forest tree of early Alzheimer’s detection.
Figure 3.17 Confusion matrix.
Figure 3.18 Pre-processing Transverse. (a) Raw/original image. (b) Skull-strippi...
Figure 3.19 Smoothing of a Raw MR Imaging.
Figure 3.20 Alzheimer’s 4-class images information.
Figure 3.21 Classification objective.
Figure 3.22 Fully connected CNN.
Figure 3.23 Flowchart of proposed model.
Figure 3.24 8-layered AlexNet architecture.
Figure 3.25 VGG16 architecture with 16 layers.
Figure 3.26 Inception module.
Figure 3.27 GoogLeNet.
Figure 3.28 ResNet architecture with skip connections running parallel.
Figure 3.29 MobileNetV2 architectural representation.
Figure 3.30 Neural Architecture Search (NASNet).
Figure 3.31 ROC curve and predictions.
Figure 3.32 Machine Learning Algorithms Comparison.
Figure 3.33 Predicting the Alzheimer’s progression: (a) Nondemented (99.13%), (b...
Figure 3.34 Validation status.
Figure 3.35 Confusion matrix.
Figure 3.36 Metric measures of MobileNetV2 during training and validation.
Chapter 4
Figure 4.1 Image segmentation techniques.
Figure 4.2 Process for identifying and detecting retinal diseases.
Figure 4.3 Extraction of gray-scaled green channel of image taken from DRIVE dat...
Figure 4.4 Input image passing through different pre-processing stages. (a) Inpu...
Figure 4.5 Output image of pre-processing stage passing through different phaseb...
Figure 4.6 Output of images of segmentation stage with various options [18].
Figure 4.7 Segmented image of proposed method [18] vs gold standard image for CH...
Figure 4.8 ROI extraction of proposed method [34] on a given input image taken f...
Figure 4.9 Results of OD segmentation of proposed method [34].
Figure 4.10 Results of OC segmentation of proposed method [34].
Chapter 5
Figure 5.1 Neural Network and Fuzzy Systems (NNFS) development contains five ste...
Figure 5.2 Methodology of ML for healthcare data analytics.
Figure 5.3 Use ML algorithms for several evaluation analyses based on storage da...
Figure 5.4 Conceptual architecture of big data analytics for health informatics.
Figure 5.5 Hadoop system architecture.
Figure 5.6 MapReduce procedure.
Chapter 6
Figure 6.1 Framework of AI.
Figure 6.2 Basic framework of an expert system.
Chapter 7
Figure 7.1 Representing of infection spreading in COVID-19.
Figure 7.2 (a) Initially all nodes are healthy with black color.
Figure 7.2 (b) After attacking, virus nodes are represented in red color. (c) In...
Figure 7.2 (d) Virus transmission without social distance leads to more red colo...
Figure 7.2 (f) Deploying antivirus in some nodes which are healthy and represent...
Figure 7.2 (h) Antivirus will spread over a period of time then slowly red will ...
Figure 7.2 (j) Sample out of proposed model for population 200.
Figure 7.3 (a) Covid spreading without social distancing, clearly shows that eff...
Figure 7.3 (b) Covid spreading with social distancing, clearly shows that effect...
Chapter 8
Figure 8.1 Structure of blockchain.
Figure 8.2 Layered approach for stakeholders.
Figure 8.3 Data divided among various stakeholders.
Figure 8.4 Ethereum Blockchain for patient–doctor interaction.
Figure 8.5 Ethereum Blockchain use case for patient, doctor and pharmacy interac...
Figure 8.6 Flow of process for reports.
Figure 8.7 Drug inventory and supply chain management.
Figure 8.8 Workflow of the health insurance companies.
Figure 8.9 Flowchart for automated flow of diagnosis.
Chapter 9
Figure 9.1 Proposed architecture for level visualization.
Figure 9.2 Contact rate estimator.
Figure 9.3 Epidemiological layer.
Figure 9.4 Relative errors of the prediction of the number of infected, recovere...
Figure 9.5 Relative error of the number of infected patients compared with SIDAR...
Figure 9.6 Illustrates a sharp drop in contact rate over lockdown.
Figure 9.7 Analysis of the predicted total number of infected individuals by dec...
Chapter 10
Figure 10.1 e-Health working process with doctor communicates the prescription t...
Figure 10.2 Big Data-Data Processing.
Image Source:
https://www.congruentsoft.co...
Chapter 11
Figure 11.1 Potential continual learning algorithms.
Figure 11.2 The four stages of drug development, along with Phase IV.
Figure 11.3 Drug development cost.
Figure 11.4 Stages of ML in radiology.
Figure 11.5 Medical imaging AI specialists by clinical application.
Figure 11.6 Medical imaging AI specialists by clinical application department wi...
Figure 11.7 Digital pathology market maturity and growth forecast.
Figure 11.8 Centralized clinical data hubs.
Chapter 12
Figure 12.1 Conceptual framework from literature review.
Guide
Cover
Table of Contents
Title Page
Copyright
Preface
Begin Reading
Index
End User License Agreement
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Big Data Analytics and Machine Intelligence in Biomedical and Health Informatics
Concepts, Methodologies, Tools and Applications
Edited by
Sunil Kumar Dhal
ASubhendu Kumar Pani
Srinivas Prasad
and
Sudhir Kumar Mohapatra
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Preface
Introduction
The novel applications of Big Data Analytics and machine intelligence in biomedical and healthcare sector can be regarded as an emerging field in computer science, medicine, biology application, natural environmental engineering, and pattern recognition. The use of various Data Analytics and intelligence techniques are nowadays successfully implemented in many healthcare sectors. Biomedical and Health Informatics is a new era that brings tremendous opportunities and challenges due to easily available plenty of biomedical data. Machine learning presenting tremendous improvement in accuracy, robustness, and cross-language generalizability over conventional approaches. The aim of healthcare informatics is to ensure the high-quality, efficient healthcare, better treatment and quality of life by efficiently analyzing the abundant biomedical, and healthcare data. Earlier, it was common requirements to have a domain expert to develop a model for biomedical or healthcare; but now the patterns are learned automatically for prediction. Due to the rapid advances in intelligent algorithms have established the growing significance in healthcare data analytics. The IoT focuses to the common idea of things that is recognizable, readable, locatable, controllable, and addressable via the Internet. Intelligent Learning aims to provide computational methods for accumulating, updating and changing knowledge in the intelligent systems and particularly learning mechanisms that help us to induce knowledge from the data. It is helpful in cases where direct algorithmic solutions are unavailable, there is lack of formal models, or the knowledge about the application domain is inadequately defined. In Future Big data analytics has the impending capability to change the way we work and live. With the influence and the development of the Big Data, IoT concept, the need for AI (Artificial Intelligence) techniques has become more significant than ever. The aim of these techniques is to accept imprecision, uncertainties and approximations to get a rapid solution. However, recent advancements in representation of intelligent system generate a more intelligent and robust system providing a human interpretable, low-cost, approximate solution. Intelligent systems have demonstrated great performance to a variety of areas including big data analytics, time series, biomedical and health informatics etc.
This book covers the latest advances and developments in health informatics, data mining, machine learning and artificial intelligence, fields which to a great extent will play a vital role in improving human life. All the researchers and practitioners will be highly benefited those are working in field of biomedical, health informatics, Big Data Analytics, IoT and Machine Learning. This book would be a good collection of state-of-the-art approaches for Big Data and Intelligent based biomedical and health related applications. It will be very beneficial for the new researchers and practitioners working in the field to quickly know the best performing methods. They would be able to compare different approaches and can carry forward their research in the most important area of research which has direct impact on betterment of the human life and health. This book would be very useful because there is no book in the market which provides a good collection of state-of-the-art methods of Big Data, machine learning and IoT in Biomedical and Health Informatics. Various models for biomedical and health informatics is recently emerged and very unmatured field of research in biomedical and healthcare. This book would be very useful because there is no book in the market which provides a good collection of state-of-the-art methods of for Big data analytics based models for healthcare.
Organization of the Book
The 12 chapters of this book present scientific concepts, frameworks and ideas on biomedical data analytics from the different biomedical domains. The Editorial Advisory Board and expert reviewers have ensured the high caliber of the chapters through careful refereeing of the submitted papers. For the purpose of coherence, we have organized the chapters with respect to similarity of topics addressed, ranging from issues pertaining to the internet of things for biomedical engineering and health informatics, computational intelligence for medical data processing and Internet of medical things.
In Chapter 1, “An Introduction to Big Data Analytics Techniques in Healthcare”. Anil Audumbar Pise presents the use of big data analytics in medicine and healthcare which is incredibly powerful, productive, interesting, and diverse. It integrates heterogeneous data like medical records, experimental, electronic health, and social data in order to explore the relations among the different characteristics and traces of data points like diagnoses and medication dosages, along with information such as public chatter to derive conclusions about outcomes. More diverse data needs to be combined into big data analysis, such as bio-sciences, sensor informatics, medical informatics, bioinformatics, and health computational biomedicine to get the truth out of its information.
In Chapter 2, “Identify Determinants of Infant and Child Mortality Based Using Machine Learning: Case Study on Ethiopia” Sudhir Kumar Mohapatra, Srinivas Prasad, Getachew Mekuria Habtemariam, Mohammed Siddique developed predictive models using four supervised machine learning techniques namely C5.0 Decision tree, Random Forest, Support Vector Machine and Naïve Bayes algorithms using the 2016 EDHS dataset of 10,641 records. The Ethiopian government doing for the past two decades for attaining millennium development goals agenda for preventing childhood mortality by improving the child health’s to change the country image to the rest of the world in reduction of childhood mortality. This study contributes some values in the improvement of childhood health by analyzing the determinants infant and child mortality by using machine learning techniques. Different reports indicate that the distribution of childhood mortality differs in the world.
In Chapter 3, “Pre-Trained CNN Models in Early Alzheimer’s Prediction Using Post-Processed MRI” Kalyani Gunda and Pradeepini Gera test MRI Scan with Dementia or Not by Non-image MRI Evidence using Random Forest Classifier which obtained 87% accuracy without false prediction and also by predicting Alzheimer’s Progression using advanced CNN models. Gentle Dementia is more focused to train the Early Detection by omitting converted MRI Sessions. Various Transfer Learning Deep Neural Networks like Residual Network (ResNet50), GoogleNet, VGG19 (Visual Geometric Group), MobileNet, AlexNet is compared to classify Alzheimer’s. Model comparison evaluated to explicate model efficacy.
In Chapter 4, “Robust Segmentation Algorithms for Retinal Blood Vessels, Optic Disc, and Optic Cup of Retinal Images in Medical Imaging” Birendra Biswal, Raveendra T., Dwiti Krishna Bebarta, Geetha Pavani P. and P.K. Biswal discussed the several robust segmentation algorithms such as a new statistical-based Kurtosis test, a novel hybrid active contour method with a new pre-processing technique is applied to fundus images of human eyes for observing the changes in Retinal Blood Vessels and Optic Disc & Optic Cup to classify as healthy or diseased eyes. For validating all these robust segmentation algorithms standard metrics are used in evaluating the performance of segmentation models. Consequently, the experimental result and comparison analysis are presented to estimate the efficacy of the proposed algorithm. As a result, standard metrics of the proposed algorithm were compared with many other previous methods suggested by various researchers and it is confirmed as to attain better efficacy values.
In Chapter 5, “Analysis of Healthcare Systems Using Computational Approaches” Hemanta Kumar Bhuyan and Subhendu Kumar Pani highlight recent contributions and efficiency of AI and ML in computer systems development for better healthcare and precision medicine. Despite various traditional and AI-based solutions, current healthcare constraints and challenges include uneven distribution of resources towards the future of digital healthcare. Unmet clinical research and data analytics requires the development of intelligent and secure systems to support the transformation of practices for the worldwide application of precision medicine. Overarching goals include new multifunctional platforms that incorporate heterogeneous clinical data from multiple platforms based on clinical, AI, and technical premises. It must address possible challenges that continue to slow the progress of this breakthrough approach.
In Chapter 6, “Expert Systems in Behavioral and Mental Healthcare: Applications of AI in Decision-Making and Consultancy” Shrikaant Kulkarni Present the latest technological advancements so as to showcase futuristic challenges and a glance at potential innovations on the horizon. The treatise enumerates the expert systems in behavioral and mental healthcare areas. It also further discusses the benefits AI can offer to behavioral and mental healthcare.
In Chapter 7, “A Mathematical-Based Epidemic Model to Prevent and Control Outbreak of Corona Virus 2019 (COVID-19)” Shanmuk Srinivas Amiripalli, Vishnu Vardhan Reddy Kollu, Ritika Prasad, Mukkamala S.N.V. Jitendra provide a preliminary evolutionary graph theory based mathematical model was designed for control and prevention of COVID-19. In the proposed model, well known technique of social distancing with different variations are implemented. Lockdown by many countries leads to the decrease of Gross Domestic Product (GDP) and increase in mental problems in citizens. These two problems can be solved by the administration of antivirus in some form to the public as a counterpart to the virus. This model works more effectively with high percolation of antiviral nodes in a population and over a period of time. There should be an exponential growth of antivirus nodes to heal the infected population.
In Chapter 8, “An Access Authorization Mechanism for Electronic Health Records of Blockchain to Sheathe Fragile Information” Sowjanya Naidu K. and Srinivasa L. Chakravarthy focuses on maintaining the patient records in the blockchain immutable ledger which allows the doctors to upload the patient records and give access to other doctors and also impose certain rights to the patients to revoke the access to everyone which provides security to the patient's records. This can also be extended to the insurance providers where they use the immutable ledger of the Electronic Health Records chain to check the patient's records and payments. Block chain technology allows the patients to assign access rules for their medical data. Block chain technology is expected to improve the Electronic Health records management and the claim process by the insurance agencies also. Not only does the Blockchain enhance the security of the data but it also helps to reduce the long and tedious process of the interhospital transfers and simplifies the process of record keeping of the Electronic Health Records. This work is beneficial to many stakeholders who are related to the medical system to carry better health services and provide security to the user's rights of protecting the data. An attempt has been made to design a framework for the individuals to access the data on the blockchain. The frameworks propose a layered approach for accessing the data of the patient by different stakeholders.
In Chapter 9, “An Epidemic Graph's Modeling Application to the COVID-19 Outbreak” Hemanta Kumar Bhuyan and Subhendu Kumar Pani present a novel machine learning approach that can estimate any epidemiological model's parameters based on two types of information: either static or dynamic. It primarily utilizes the Graph model using deep learning approaches and Long-term memories (LSTMs) to obtain mobility data's spatial and temporal properties of SIR and SIRD models. It runs and simulates using data on the Italian COVID dynamics and compares the model predictions to previously observed epidemics.
In Chapter 10, “Big Data and Data Mining in e-Health: Legal Issues and Challenges” Amita Verma and Arpit Bansal focus on the legal framework with respect to privacy in India and a comparison of the same with other countries. E-Health is a rising industry. At a time when physical healthcare facilities are full of COVID19 patients, the e-Health Industry has become even more diverse and is being resorted to as primary healthcare system specially to treat regular health problems. The health data of millions of patients is being stored online. The same is done through the concept of Big Data and Data Mining in e-Health. In India, National Digital Health Mission is aimed towards the use of this technique to simplify e-Health services.
In Chapter 11, “Basic Scientific and Clinical Applications” Manna Sheela Rani Chetty and Kiran Babu C. V. discuss the various applications and its significant advancements in medicine and health care. Appling the principles of computer science and information science to the advancement of research in the area of life sciences, health professions education, public health, patient care, etc. can be considered as biomedical and health informatics (HI). The integrative field and multidisciplinary focuses on health information technologies, and involves the computer, cognitive, and social sciences. Informatics is one of the sciences which reflects how to use data, information and knowledge to improve human health and the delivery of health care services. HI studies the effective use of probabilistic information for decision making. The combination of both has greatest potential to rise quality, efficacy and efficiency of treatment and care.
In Chapter 12, “Healthcare Branding Through Service Quality” Saraju Prasad and Sunil Dhal offer a deep insight into the service quality model dimensions in healthcare. In India the healthcare services can be divided into two categories like public and private healthcare services. The Public Healthcare System (PHC) which is under the control of government is available in cities and rural areas and provides services mostly primary services. Majority of the private sector healthcare service providers are in metropolis, capital cities and few others cities of the country mostly focused on secondary and tertiary services. India got the competitive advantages in maximum number of experienced medical practitioners.
Concluding Remarks
The chapters of this book were written by eminent professors, researchers and those involved in the industry from different countries. The chapters were initially peer reviewed by the editorial board members, reviewers, and those in the industry, who themselves span many countries. The chapters are arranged to all have the basic introductory topics and advancements as well as future research directions, which enable budding researchers and engineers to pursue their work in this area.
Big Data Analytics and machine intelligence in biomedical and health informatics is so diversified that it cannot be covered in a single book. However, with the encouraging research contributed by the researchers in this book, we (contributors), editorial board members, and reviewers tried to sum up the latest research domains, developments in the data analytics field, and applicable areas. First and foremost, we express our heartfelt appreciation to all the authors. We thank them all for considering and trusting this edited book as the platform for publishing their valuable work. We also thank all the authors for their kind co-operation extended during the various stages of processing of the manuscript. This edited book will serve as a motivating factor for those researchers who have spent years working as crime analysts, data analysts, statisticians, and budding researchers.
Dr. Sunil Kumar DhalProfessor IT, Sri Sri University, Odisha, India
Dr. Subhendu Kumar PaniPrincipal, Krupajal Computer Academy, BPUT, India
Dr. Srinivas PrasadGITAM Institute of Technology, Visakhapatnam Campus, India
Dr. Sudhir Kumar MohapatraAddis Ababa Science and Technology University, Addis Ababa, Ethiopia March 2022
1An Introduction to Big Data Analytics Techniques in Healthcare
Anil Audumbar Pise*
University of the Witwatersrand, Johannesburg, South Africa
Abstract
There is a notable rise in the amount of data being generated in the healthcare industries. Trying to improve the health outcomes and cut the costs derived from better utilization of healthcare data has been of great interest to healthcare providers (and the abundance of the data has brought that about big change), whereas the nature of healthcare data presents specific problems when it comes to processing and looking at big data, particularly, as well as analyzing the abundance of it. Some new ideas about how to deal with these problems are discussed in this chapter. According to this chapter, there are two ways in which advances in processing healthcare data have been made in the last 10 years that may make generating better predictions from the medical data feasible. Firstly by using advancing technological methods of analysis and secondly developing novel models that can handle large quantities of data.
Keywords: Healthcare analytics, predictive analytics, healthcare informatics, big data
1.1 Introduction
Big Data has the potential to transform all sorts of business sectors, from the wellness of individuals to the provision of healthcare. For the purposes of most current day, Big Data is defined as “storing, arranging, and processing, the current huge amounts of heterogeneous data, getting results, and then reorganized and measured data is called Clean/Big Data”. This pattern emerges because businesses are using technology to accomplish more and to help customers generate more data which creates a greater volume of data that consumers then produce, who generate bigger volumes of data in social networks. A variety of new developments involving more modern sources and different ways of processing data is currently emerging in the healthcare and medical industries. One thing is clear from the research point of view is the field of ‘omics’ in which previously used, pre-owned data offers new approaches to e-health records, open data, and the ‘quantified self’ methodologies for enhancing data analytics. We have made tremendous advances in text data extraction, which unlocks a lot of information in the medical records for analytics. On the other hand, big data use in healthcare, adoption of new medical and healthcare practices are moving more slowly than people may be expected. These difficulties can be found to their varying levels of data complexity, to issues regarding data, organization, and regulations, and also issues concerning ethical issues. It is very likely that new ideas and better practices for data acquisition and data analysis will emerge from larger scales of the accumulation of big data and the best practices. This paper takes a comprehensive look at the possibilities of Big Data holds for the medical and healthcare professions.
Although big data analytics is relatively new in its role in-flux in healthcare, it is nevertheless having a significant impact in practices and research. The system has given healthcare researchers the ability to gather, store, and manage disparate, structured, and unstructured data generated by current healthcare systems, as well as data sets for analysis. Larger databases and powerful computer software have recently been used in medical research to help with delivery and disease exploration. Some of the most basic big data principles cannot be escaped, even though advances have been made; as long as there are these limitations, they may persist in preventing further development in this sector. A concern that we wanted to tackle in this paper is the obstacles we encounter in three exciting new and emergent medical research areas: Genomic Data Analysis, Signal Detection, and Medical Image Processing. In the most recent studies, the focus has been on employing high volume data of medical information, which integrates multimodal information from diverse sources. In order to evaluate the capabilities and opportunities for healthcare delivery, research focuses on areas with the ability to make a positive difference as well as well as potential.
The remainder of this chapter is organized as follows. In Section 1.2, a brief idea of Big Data in Healthcare is explained with basic introduction and concept of the five Vs of big data with aspects that explore the use of big data in medical field. In Section 1.3, Areas of Big Data Analytics in medicine are discussed. In Section 1.4, the Concept of Healthcare a Big Data Repository is briefly explained. Then, Section 1.5 presents Applications of Healthcare Big Data with examples and in Section 1.6 Challenges in Big Data Analytics are provided. Big Data Privacy and Security policies are explained in Section 1.7. The remaining sections provide a conclusion and future work.
1.2 Big Data in Healthcare
The term “Big Data” refers to the volume, velocity, and variety of data generated over time by healthcare providers and containing information pertinent to a patient’s care, such as demographics, diagnoses, medical procedures, medications, vital signs, immunizations, laboratory results, and radiology images. Figure 1.1 depicts above mentioned healthcare entities.
Figure 1.1 Big data in healthcare.
Figure 1.2 Five vs of big data.
According to Thota et al. [1], electronic health sources such as sensor devices, streaming machines, and high-throughput instruments are accumulating more data as medical data collection advances. This big data in healthcare is used for a variety of purposes, including diagnosis, drug discovery, precision medicine, and disease prediction. Big data has been critical in a variety of fields, including healthcare, scientific research, industry, social networking, and government administration [1]. The five Vs of big data are as follows as shown in the Figure 1.2 for better understanding:
1. Variety: Without a doubt, the variety of data represents big data. For instance, among the various data formats are database, excel, and CSV, all of which can be stored in a plain text file. Additionally, structured, unstructured, and semi-structured health data exist. Clinical data is an example of structured information; however, unstructured or semi-structured data includes doctor notes, paper prescriptions, office medical records, images, and radio-graph films.
2. Veracity: This data’s legitimacy in the form of veracity can be challenged only if it is inaccurate. It is not about the accuracy of the data; it is about the capacity to process and interpretation of data. In healthcare, the trustworthiness function gives details on correct diagnosis, treatment, appropriate prescriptions, or otherwise established health outcomes.
3. Volume: Without a doubt, the large volume represents large amounts of data. To process massive amounts of data such as text, audio, video, and large-format images, existing data processing platforms and techniques must be strengthened. Personal information, radiology images, personal medical records, genomics, and biometric sensor readings, among other things, are gradually integrated into a healthcare database. All of this information adds significantly to the database’s size and complexity.
4. Velocity: Big data is completely represented by the amount of information produced every second is considered as velocity. The information burst of social media has brought about a wide range of new and interesting data. Data on overall health condition and growth of the plant size and food bacteria are stored on paper, as well as various X-ray images and written reports, is up dramatically.
5. Value: Big data truly embodies the value of data. When it comes to big data analytics, the benefits and costs of analyzing and collecting big data are more important. In healthcare, the creation of value for patients should dictate how all other actors in the system are compensated. The primary goal of healthcare delivery must be to maximize value for patients.
1.3 Areas of Big Data Analytics in Medicine
It is of critical importance to pay attention to a multitude of events that impact the health, both physiologically and pathologically. Occurring at once and expressed in various ways (systemic) aspects of the body lead to interaction between different cardiovascular parameters (i.e. such as minute ventilation and blood pressure) which results in accurate clinical evaluation. As a result, understanding and predicting diseases necessitate an integrated data collection of both structured and unstructured methods that draw on the enormous spectrum of clinical and non-clinical data to create a more thorough picture of disease depiction. Big data analytics has recently made its entrance into the healthcare industry, medical researchers are excited about an entirely new aspect of this research known as incorporating the newer concepts. Researchers are conducting research on healthcare data pertaining to both the data itself and the taxonomy of useful analytics that can be done on it.
Figure 1.3 Areas of big data analytics in medicine.
Expanding on this one would include three areas of big data analytics in medicine which is discussed in this chapter. These three research areas do not comprehensively showcase the many ways big data analytics are applied in medicine; instead, they provide a collection of loosely defined use cases where big data analytics is being employed as shown in Figure 1.3.
1.3.1 Genomics
In [2] the author suggested that the estimated price of sequencing the human DNA (the “combing cost” of) has dropped significantly in the past few years [cost to combing the 30,000 to 35,000 gene map is now inversely proportional to how many genes are found] on the grand scale, and as it is to computational biology, developing genome-scale solutions that are applied to the field of public health can have implications for current and future public health policies and services. In 2013 [3] researcher claimed that, the most important factor in making recommendations in a clinical setting is the cost and time to put them in place. Prospective/preventive, and proctical health-focused strategies aim to acquire information on 100,000 individuals for more than two decades, known as P4-predicted (stating only if it is possible); research using the predictive-targeted, or integrated omics, referred to as personalomics (using your personal data). In [4] the author suggested to include seeking solutions over with regard to the following four aspects such as:
1. Developing scalable genome-scale data states
2. Use of tools
3. Clinical states
4. Data challenges in target validation, and integration, a big data project.
Project (P4) is making strides by acquiring tools to help with handling massive datasets, and then, following this, they have developed continuous monitoring tools that aid in understanding a subject’s condition, as well as obtaining new information, and they are moving forward in their search for medication delivery and analytical tools. Everything that is known about a person’s physiology and his/her physiological states in-based person wellness is summarized and is added to person omics (usage-driven genomics methods) which are used to identify and detail the subject’s medical state [5]. Although an actionable course of action at the level of care may be one of the most difficult aspects, many improvements at the clinical level can be pursued (even though it may be arduous). According to [6], a lot of high-resolution data is required for exploration, discovery, and implementing novel approaches. These two aspects of big data necessitate the use of novel data analytics.
1.3.2 Signal Processing
Medical signals like medical images present volume and velocity challenges, most notably during continuous, high-resolution acquisition and storage from a plethora of monitors connected to each patient. Additionally, the problem of size is posed by physiological signals in that they possess a size/physical dimension in time and space. In order to derive the most useable and appropriate responses from physiological data, an individual must be aware of the circumstances that are affecting the measurements and have continual monitoring to be established in place to assure effective use and robustness, rigorous monitoring of those variables is required.
Currently, healthcare systems rely on a patchwork of disparate and continuous monitoring devices that use single physiological waveform data or discretized vital information to generate alerts in the event of over events [7]. However, such uncomplicated approaches to developing and implementing alarm systems are inherently unreliable, and their sheer volume may result in “alarm fatigue” for care givers and patients alike [8, 9]. In this context, the capacity for new medical knowledge discovery is constrained by prior knowledge that has frequently fallen short of fully exploiting high-dimensional time series data. In [10] Jphan et al. suggested the reason these alarm mechanisms frequently fail is that they rely on isolated sources of information and lack context regarding the patients’ true physiological conditions from a broader and more comprehensive perspective. As a result, improved and more comprehensive approaches to studying interactions and correlations between multimodal clinical time series data are required. This is critical because research consistently demonstrates that humans are unable to reason about changes affecting more than two signals [11, 12].
1.3.3 Image Processing
Medical images are a valuable source of data that are frequently used for diagnosis, treatment evaluation, and planning [13]. Computed Tomography (CT), Magnetic Resonance Imaging (MRI), Photoacoustic Imaging (PI), Molecular Imaging (MI), Positron Emission Imaging (PEI), and Sonography are all examples of established clinical imaging techniques. However, medical image data will often have up to hundreds of megabytes (e.g., up to 2,500+ scans [14]) for one study (in one study, for example, histology data), or even thousands of megabytes (a large number of scans in a thin-slice CT study, e.g., proctology). Data needs a large storage area to be held for extended periods of time. While any decision support needs to be completed on the fly, they must be quick and precise algorithms in order to have practical benefits. Even though these patients’ overall and individual medical data are often acquired for each of giving additional information, as well as for their diagnoses, prognoses, treatment procedures, and outcomes, the development of storage and methodologies capable of gathering and maintaining relevant medical data is additionally challenged.
Despite current healthcare systems’ enormous expenditures, clinical outcomes remain sub-optimal, particularly in the United States of America, where 96 people per 100,000 die annually from treatable conditions [15]. A significant contributor to such inefficiencies is healthcare systems’ inability to effectively collect, share, and use more comprehensive data [16]. This creates an opportunity for big data analytics to play a more significant role in assisting the exploration and discovery process. Improving care delivery, assisting in the design and planning of healthcare policy, and providing a means of comprehensively measuring and evaluating the complicated and convoluted healthcare data. More importantly, the adoption of insights gleaned from big data analytics has the potential to save lives, improve care delivery, expand access to healthcare, align payment with performance, and aid in containing the perplexing growth of healthcare costs.
1.4 Healthcare as a Big Data Repository
Healthcare is a multi-dimensional system established with the sole aim for the prevention, diagnosis, and treatment of health-related issues or impairments in human beings. These are the main parts of a healthcare system; you have the medical personnel (doctors and nurses), which supports the healthcare facilities (clinics and hospitals for delivering medicine and technologies), and then you have financing supporting them. The physicians who practice in different areas of healthcare, such as dentistry, midwifery, and psychology are health professionals known as so-aspirants. Since there are so many issues with healthcare, it depends on the level of urgency and extent of treatment to expand. The professional first; their clientele receives it from a variety of treatment options, complex and invasive conditions, both from non-professional physicians and private hospitals, and as well as from the general medical community (non-specialized as well as well as private) (quaternary care) [17]. A doctor, nurse, researcher, radiologist, and lab technician are all needed to have separate needs and are held responsible for a number of different types of information. For example, that of patient history (diagnosis and prescriptions), other medical and clinical (data obtained from imaging and laboratory tests), and personal history (all those that may apply), data on other medical issues as opposed to previous record keeping methods that typically utilized handwritten or typed case notes, in which these medical records were stored. This earlier method was not done, this could be compared to the results of a medical tests which are traditionally kept in an inadequate electronic systems. For reference, an ancient papyrus from Egypt suggests that this was standard practice even a figure in the time of 1600 BC [18]. In Stanley Reiser’s opinion [19], the medical case histories do an excellent job of recording everything in relation to the story of the patient, the family, and the physician, while preserving the dynamics of the illness.
Although digital systems have long been commonplace in healthcare, the implementation of more complex medical records is considered modern—a “means” today for generating an expanded comprehension of the available data sets and further learning about that health and illness. In 2003, the Institute of Medicine, a division of the National Academies of Sciences, Engineering, and Medicine, coined the term “Electronic Health Records” to refer to records maintained for the purpose of improving the healthcare sector for the benefit of patients and clinicians. As defined by Murphy, Hanken, and Waters, Electronic Health Records (EHRs) are computerized medical records for patients that contain information about an individual’s past, present, or future. Physical/mental health or condition that is stored in electronic systems that are used to capture, transmit, receive, store, retrieve, link, and manipulate multimedia data for the primary purpose of providing healthcare.
1.5 Applications of Healthcare Big Data
There are new applications that can make use of big data sets to explore various avenues of knowledge, and there are methods to refine healthcare delivery to be derived from these discoveries (crucial uses, noxerous applications). Some critically important ones include the application of public health, clinical use, medicine based on scientific evidence, and medical diagnosis, and verification, analysis, and patient monitoring. These are the various healthcare frameworks and healthcare storage systems that were briefly explained to explore applications of healthcare big data below.
1.5.1 Electronic Health Records (EHRs)
Electronic Health Records (EHRs) is by far the most prevalent use of big data in medicine. Each patient has his or her own digital record, which contains demographic information, medical history, allergies, and laboratory test results, among other things. Records are shared securely via information systems and are accessible to both public and private sector providers. Each record is composed of a single modifiable file, which enables doctors to make changes over time without incurring additional paperwork or risk of data replication.
Additionally, EHRs can generate alerts and reminders when a patient requires a new lab test or track prescriptions to ensure the patient is following doctors’ orders. While EHRs are an excellent idea, many countries have yet to fully implement them. The United States has made significant strides, with 94% of hospitals adopting EHRs, according to this HITECH research, but the European Union continues to lag behind. However, an ambitious directive being drafted by the European Commission is intended to alter that situation.
Kaiser Permanente is setting the standard in the United States and may serve as a model for the EU. They’ve fully implemented a system called Health Connect, which allows data to be shared across all of their locations and simplifies the use of EHRs. According to a McKinsey report on big data healthcare, the integrated system has improved cardiovascular disease outcomes and saved an estimated $1 billion through reduced office visits and lab tests.
1.5.2 Telemedicine
Television conferences, smartphones, and other wireless devices, and wearables being able to provide on-the-demand healthcare have recently brought about a major advancements in medical field using “Telemedicine”. A “Telemedicine” term is used to describe healthcare and treatment facility via electronic devices. Electronic or satellite technologies are used for the delivery of clinical services that are not close to where patients are located.
Physicians use it for primary consultations, for early detection, for the development of disease, and for educating their colleagues, and as a tool for remote monitoring. While some uses, like robotic surgery, tele-surgery, allow them to operate at a quicker pace with high-resolution data feedback, these do not require the doctor and patient to be in the same location; others like ultrasonography allow for the use of wider applications like fast-molecular imaging/live motion, still apply the principle of real-time feedback.
Clinicians deliver highly personalized treatment plans as well as helping to keep patients out of the hospital. Prior to this most healthcare organizations had used analytical techniques such as demographics, maps, databases, and graphical presentations in conjunction with predictive analytics to investigate issues related to healthcare delivery system growth and geographical issues. Additionally, by making early judgments on how the patient will respond to changes in his or her condition, this helps clinicians predict acute illnesses before they become worse.
By keeping patients away from hospitals, telemedicine helps greatly and reduces the cost while improving service quality. Patients don’t have to wait in line and doctors don’t make time wasting in line or dealing with unnecessary paperwork a priority. Telemedicine particularly improves access to care, since monitoring of patients’ physical conditions is now possible no matter where they are, at any time.
1.5.3 NoSQL Database
Nowadays, much information is given to all databases, with the end result that it exists in multiple locations for purposes that are general in nature. There is no relationship between the tabular and non-relational schema in a NoSQL database. To those of you are not aware, there are many different NoSQL databases that are categorized into four main types:
1. Those based on the keys (Key-value).
2. In-memory (Family) in which column-store (file-family).
3. Document-store (Data-memory).
4. Graph-store (Data in memory).
It’s good for simple data that is only read rarely, but has the potential to expand to contain more data because of its expandability. It keeps vast amounts of data in one column family; in other words, the column family database stores huge numbers of individual columns all at once. The semi-structured data contains vast amounts of information pertaining to document formats, with regard to the documents in it, or data (opinions, theories, opinions, or interpretations). The last thing on the graph is the inversion of an N-to-M relationship, which is a Q-to-M relationship which is recorded as an N-to-entry database.
1.5.4 Framework for Reconstructing Epidemiological Dynamics (FRED)
It is an open-source modeling that can apply to a wide variety of disease patterns. Every resident, office holder, owner, and entity location (entities which hold locations or locations which hold entities) is listed in the regional economic data system FRED. Each agent is distinguished by both by their personal sociodemographic features (such as whether they are working, have a sex, and reside in a particular residence) and their daily activities (their occupational, for example). The experimental populations that are used in the FRED simulation to work out the potential spread of disease are called artificial populations.
1.5.5 Advanced Risk and Disease Management
The hospitalization risk for a specific patient could be tackled using big data and healthcare; it is something we cannot avoid. It is also an excellent way to preserve the original. The use of more general information is readily available to any institution, like the type of medication used. The number of illnesses and the amount of visits enable healthcare providers to provide more precise treatments and ultimately reduces the rate of hospitalization. While space and resources will be available for the healthiest patients, this degree of risk calculation will also mean that expenses will be kept down for in-house and thus enhance the chances of maintaining our practice’s financial security. This is a real-world demonstration of how analytics in healthcare can be used to help and save lives.
Measuring and identifying factors such as genes, proteins, cell membrane and organ systems, the immunology of specific diseases, and epidemiology can also expand their capacity for care by reducing operating more economically while improving the quality of data management costs across the healthcare field.
1.5.6 Digital Epidemiology
There is a form of epidemiology known as Digital Epidemiology that incorporates digital methods from data collection to analyze data. It boosts epidemiological methods, such as case reports, control group studies, and ecologic studies. It makes use of case studies, ecological studies, and crosstype studies that include cases in its investigation and a mix of controlled trials and cohort studies, such as separate cohorts and ecological case studies. It also makes use of data sourced from other sectors such as data sets that were originally developed for health purposes or information sets.
1.5.7 Internet of Things (IoT)
There was previously only one model for patients interacting with doctors i.e. in person and via telephone or tele and text messaging. There was no way that doctors and hospitals could monitor patient health continuously, and thus be able to give prescriptions appropriately.
Innovations that can assist patients and clinicians in the ability to keep them safe and enhance care with smart equipment enabled by the Internet of Things (IoT) offers new possibilities for monitoring people in the medical field. At the same time, it has resulted in patient engagement and satisfaction because physician and patient communications have become more straightforward. Furthermore, patients’ health can be tracked and therefore, reduces their length of stay and lowers their likelihood of having to return to the hospital after discharge. Widespread implementation of the IoT can help lower healthcare costs and increase treatment effectiveness.
It is almost certain that the healthcare industry will be changed by how it connects with devices and the physical bodies of people by means of Internet of Things. It has applications in the healthcare industry, as well as being beneficial to patients, family members, physicians and hospitals.
1.5.7.1 IoT for Health Insurance Companies
Healthcare devices are rapidly becoming more connected, and thus many approaches are necessary to deal with the various scenarios that may arise from that. A health-monitoring device can be used to assist in insurance under writing and operational tasks, for example, is it possible for insurance companies to leverage that data providing this information will help them detect and evaluate potential clients’ claims of fraud as well as identify those who could benefit from this method of treatment.
Insurance Information Technologies (IIT) have another significant benefit for customers. Not only are they utilized for introducing standard under writing, pricing, but they are also utilized for risk assessment. Better visibility means customers can view the information used in every decision, fostering data driven decisions. This allows companies to conduct in-based thinking in all aspects of their organization, which increases customers’ comprehension of the thought behind every decision.
Many insurance companies are researching incentives that would reward customers for utilizing and contributing to the health data generated by IoT (Internet of Things) devices. There are various potential approaches for better treatment compliance and more substantially compliant customers who use IoT devices. They could offer these services in exchange for their measured activity, which is something they have control over. This will also assist insurance companies, as they work to reduce their liability claims. As with the devices that collect data from the Internet of Things, this type may also handle claims for insurance companies, as it is feasible that they can prove payment claims for the insurance firms’ involvement.
1.5.7.2 IoT for Physicians
Instead of relying on health-measuring wearables and health-monitoring wearables are used, both, for more accurate recording of the patient’s
