Machine Learning for Healthcare Applications Sachi Nandan Mohanty
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Demystifying Big Data, Machine Learning, and Deep Learning for Healthcare Analytics Pradeep N Sandeep Kautish Sheng-Lung Peng
This edition first published 2021 by John Wiley & Sons, Inc., 111 River Street, Hoboken, NJ 07030, USA and Scrivener Publishing LLC, 100 Cummings Center, Suite 541J, Beverly, MA 01915, USA
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Library of Congress Cataloging-in-Publication Data
ISBN 9781119791812
Cover image: Pixabay.Com
Cover design by Russell Richardson
Set in size of 11pt and Minion Pro by Manila Typesetting Company, Makati, Philippines
Printed in the USA
10 9 8 7 6 5 4 3 2 1
2.4.5
5.1
6.4
8.1
8.2
9.1
N.V.S. Abhishek, Dibyajit Bardhan, Anisha Ankita Ghosh and Shubhadarshinin
K.G. Maheswari, G. Nalinipriya, C. Siva and A. Thilakesh
18.2.1
18.2.2
19.1
18.2.4
18.2.5
18.2.6 LDA Topic Modeling on Twitter to Study Public Discourse and Sentiment During the Coronavirus Pandemic
18.2.7 The First Decade of Research on Sentiment Analysis
18.2.8 Detailed Survey on the Semantic Analysis Techniques for
18.2.9 Understanding Text Semantics With LSA
18.2.10 Analyzing Suicidal Tendencies With Semantic Analysis
18.2.11
20.7
20.7.5.1
Part 5: Case Studies of Application Areas of Machine Learning
21.1.1
21.1.2
22.1
22.2
21.4.4
21.4.4.1
23.2
23.3
Biswabijayee Chandra Sekhar Mohanty, Sonali Mishra and Sambit Kumar Mishra
24.1
24.4.2
24.4.3
24.4.4
24.5
Preface
Machine learning is one of the principal components of computational methodology. In today’s highly integrated world, when solutions to problems are cross-disciplinary in nature, machine learning promises to become a powerful means for obtaining solutions to problems very quickly, yet accurately and acceptably.
When considering the idea of using machine learning in healthcare, it is a Herculean task to present before the reader the entire gamut of information in the field of intelligent systems. It was therefore our objective to keep the presentation narrow and intensive. The approach of this book is distinct from others in that it presents detailed computer simulations for all models presented with explanations of the program code. It includes unique and distinctive chapters on disease diagnosis, telemedicine, medical imaging, smart health monitoring, social media healthcare, and machine learning for COVID-19. These chapters help develop a clear understanding of the working of an algorithm while strengthening logical thinking. In this environment, answering a single question may require accessing several data sources and calling on sophisticated analysis tools. While data integration is a dynamic research area in the database community, the specific needs of research have led to the development of numerous middleware systems that provide seamless data access in a result-driven environment.
Since this book is intended to be useful to a wide audience, students, researchers and scientists from both academia and industry may all benefit from this material. It contains a comprehensive description of issues for healthcare data management and an overview of existing systems, making it appropriate for introductory and instructional purposes. Prerequisites are minimal; the readers are expected to have basic knowledge of machine learning.
This book is divided into 22 real-time innovative chapters which provide a variety of application examples in different domains. These chapters illustrate why traditional approaches often fail to meet customers’ needs. The presented approaches provide a comprehensive overview of current technology. Each of these chapters, which are written by the main inventors of the presented systems, specifies requirements and provides a description of both the chosen approach and its implementation. Because of the self-contained nature of these chapters, they may be read in any order. Each of the chapters use various technical terms which involve expertise in machine learning and computer science.
The chapters of the book are organized as follows:
• Chapter 1 introduces the fundamental concepts of machine learning and its applications, and describes the setup used throughout the book. It is now realized that complex real-world problems require intelligent systems that combine knowledge, techniques and methodologies from various sources.
• Chapter 2 describes the actual machine learning algorithms that are most widely used in practice, and discusses their advantages and shortcomings. It is therefore necessary to work through conventional machine learning algorithms while relating the underlying theme to cutting-edge neuroscience research findings.
• Chapter 3 explains the study of neuromarketing with EEG signals and machine learning techniques. This is followed by a detailed review of the global function of classifiers and the inner workings. Such a premise provides the fabric for presentation of ideas throughout this text.
• Chapter 4 elaborates on an expert system-based clinical decision support system for hepatitis B prediction and diagnosis. It develops a working model of the decision support system and its application domain. The clinical decision helps to improve the diagnostic performance.
• Chapter 5 works on disease prediction to develop an intuitive understanding of fundamental design principles. These concepts are carried to their fullest complexity with neural networks and their learning. The working of artificial neurons and the architecture stands in stark contrast with their biological counterparts.
• Chapter 6 introduces machine learning as a public safety tool. A solid discussion on the relationship between public safety and video surveillance systems is provided. The topic of offline crime prevention leads to the extremely important topic of public safety, which is discussed in the context of machine learning theory.
• Chapter 7 introduces semantic web ontology, multi-agent system in a semantic framework, decision-making ontology and query optimizer agent. These unified methods open up a new avenue of research.
• Chapter 8 focuses on the detection, prediction and intervention strategies of attention deficiency in the brain. These important topics are missing from many current texts on machine learning.
• Chapter 9 summarizes the issues concerning the progression of osteoporosis using machine learning and the treatment models, and culminates in the presentation of K-nearest neighbor and decision tree algorithms.
• Chapter 10 covers the issues in biomedical text processing and the food industry. It addresses the latest topics of face recognition systems for domestic cattle, assortment of vegetables and fruits, plant leaf disease detection and approaches for sentiment analysis on drug reviews.
• Chapter 11 discusses hyperparameter tuning of the MobileNet-based CNN model and also explains ResNet5.0. It presents a variety of important machine learning concepts found in the literature, including confusion matrix and classification results.
• Chapter 12 presents a detailed introduction to the theory and terminology of deep learning, image classifier, and data preprocessing with augmentation. It talks about malaria cell detection and finally the results are tabulated in a meaningful manner for further fruitful research.
• Chapter 13 considers various approaches for the design of transfer learning, including CNN architecture with ROC curve as a core neural network
model, which can incorporate human expertise as well as adapt themselves through repeated learning.
• Chapter 14 provides a model for early stage detection. It gives a variety of application examples in different domains such as multivariate regression, model building, and different learning algorithms.
• Chapter 15 presents the concept of using the internet of things (IoT) in healthcare applications. It focuses on networking system using the IoT, smart hospital environments, emerging vulnerabilities and threat analysis.
• Chapter 16 explains real-time health monitoring. It proposes a framework for model construction, supervised learning, neural networks for classification and decision-making. An application is presented that supports health monitoring by implementing IoT concepts. A multiple linear regression algorithm and random forest algorithm are used to map the requirement of distance health monitoring.
• Chapter 17 introduces ontology in healthcare. It also explains NLP-based retrieval for COVID-19 dataset. Query formulation and retrieval from a knowledgebase are handled in an effective manner. Included are several examples in the literature to travel further in this research direction.
• Chapter 18 summarizes the topics necessary for COVID-19 research. It details the public discourse and sentiment during the coronavirus pandemic. Moreover, how to understand text semantics and semantic analysis using social media are explained.
• Chapter 19 is devoted to basic COVID-19 research and its relationship to various data mining techniques. Prediction and analysis of COVID-19 dataset, dataset collection, backpropagation neural network, and several algorithms are discussed in detail.
• Chapter 20 details automated diagnosis of COVID-19. Topics treated include the feature extraction, genetic algorithm and image segmentation technique. The presented approach provides a description of both the chosen approach and its implementation.
• Chapter 21 provides users and developers with a methodology to evaluate the present system. It focuses on the future of telemedicine with machine learning. The state-of-the-art, existing solutions and new challenges to be addressed are emphasized. Fast electronics health record retrieval, intelligent assistance for patient diagnosis and remote monitoring of patients are discussed very clearly.
• Chapter 22 discusses the challenges faced by chronic disease patients and the lightweight convolutional neural network used to address these challenges. Experimental results are tabulated, leading to active research in the healthcare field
• Chapter 23 discusses disease diagnosis. Active solutions using machine learning techniques are given along with the generalize tools used to implement the concepts. A wide range of research areas are also given for future work.
• Chapter 24 explains the detection of disease and its related solution in machine learning. The chapter continues with the treatment of machine leaning algorithms that are dynamic in nature. It presents a number of powerful
Preface
machine learning models with the associated learnings. A discussion section is provided that briefly explains what can be computed with the models.
Finally, we would like to sincerely thank all those involved in the successful completion of the book. First, our sincere gratitude goes to the chapters’ authors who contributed their time and expertise to this book. Second, the editors wish to acknowledge the valuable contributions of the reviewers regarding the improvement of quality, coherence, and content presented in the chapters.
The Editors February 2021
Part 1
INTRODUCTION TO INTELLIGENT HEALTHCARE SYSTEMS
1
Innovation on Machine Learning in Healthcare Services—An Introduction
Parthasarathi
Pattnayak1* and Om Prakash Jena2
1School of Computer Applications, KIIT Deemed to be University, Bhubaneswar, Odisha, India
2Department of Computer Science, Ravenshaw University, Cuttack, Odisha, India
Abstract
The healthcare offerings in evolved and developing international locations are seriously important. The use of machine gaining knowledge of strategies in healthcare enterprise has a crucial significance and increases swiftly. In the beyond few years, there has been widespread traits in how system gaining knowledge of can be utilized in diverse industries and research. The organizations in healthcare quarter need to take benefit of the system studying techniques to gain valuable statistics that could later be used to diagnose illnesses at a great deal in advance ranges. There are multiple and endless Machine learning application in healthcare industry. Some of the most common applications are cited in this section. Machine learning helps streamlining the administrative processes in the hospitals. It also helps mapping and treating the infectious diseases for the personalised medical treatment. Machine learning will affect physician and hospitals by playing a very dominant role in the clinical decision support. For example, it will help earlier identification of the diseases and customise treatment plan that will ensure an optimal outcome. Machine learning can be used to educate patients on several potential disease and their outcomes with different treatment option. As a result it can improve the efficiency hospital and health systems by reducing the cost of the healthcare. Machine learning in healthcare can be used to enhance health information management and the exchange of the health information with the aim of improving and thus, modernising the workflows, facilitating access to clinical data and improving the accuracy of the health information. Above all it brings efficiency and transparency to information process
Keywords: Machine learning, healthcare, EHR, RCT, big data
1.1 Introduction
The human services is one of the significant possessions inside the general public. In any case, because of expedient development social orders’ desires for human services surpass the substances of ease and reachable consideration. As need for medicinal services develops, granting enough human services to the general public is the essential need of the principles in social insurance zone. The state of the well-being zone fluctuates relying upon the
nation’s populace, social turn of events, regular sources, political and money-related gadgets. Increment of importance given to medicinal services and the excellent level of social insurance, expands resistance among well-being gatherings and offers a critical commitment to the improvement of the world. Medical problems influence human lives. During clinical thought, prosperity associations secure clinical real factors around each particular affected individual, and impact data from the overall people, to conclude how to manage that understanding. Information along these lines plays out a basic situation in tending to medical problems, and advanced insights is basic to upgrading influenced individual consideration. Without question, one of the most imperative components that influences human services area is time. In spite of speedy increment in social orders and in social orders’ requirement for medicinal services, todays’ propelling period can be one of the most essential components that can react to the need of human services contributions in social orders. Fortunately, nowadays we’ve a convoluted age in human services structures which could help settling on choices dependent on gathered information. This ability of the age in medicinal services structures is as of now becoming accustomed to aggregate information roughly any manifestation that an influenced individual has, to analyze special afflictions before they happen at the influenced individual, and to forestall any of these sicknesses with the guide of playing it safe. With the assistance of that innovation, numerous victims have just been protected from various dreadful ailments. Utilizing realities, machine considering has driven advances in numerous areas comprehensive of PC creative and judicious, NLP, and robotized discourse fame to gracefully puissant structures (For instance, engines with driver less, non-open associates enacted voice, mechanized interpretation).
Thinking about calm masses to perceive causes, chance factors, ground-breaking meds, and sub sorts of sickness has for a long while been the space of the study of disease transmission. Epidemiological systems, for instance, case-control and unpredictable controlled starters ponders are the establishments of verification upheld prescription. In any case, such techniques are dreary and expensive, freed from the inclinations they are planned to fight, and their results may not be material to authentic patient peoples [1]. All inclusive, the gathering of electronic prosperity records (EHRs) is growing a direct result of frameworks and associations that help their usage. Techniques that impact EHRs to react to questions took care of by disease transmission specialists [2] and to manufacture precision in human administrations transport are as of now ordinary [3].
Data assessment approaches widely fall into the going with classes: expressive, explorative, deductive, insightful, and causative [4]. An elucidating examination reports outlines of information without understanding and an explorative investigation distinguishes relationship between factors in an informational index. At last, a causal examination decides how changes in a single variable influence another. It is vital to characterize the sort of inquiry being posed in an offered examination to decide the kind of information investigation that is fitting to use in addressing the inquiry. Prescient examinations used to anticipate results for people by building a measurable model from watched information and utilizing this model to create an expectation for an individual dependent on their interesting highlights. Prescient displaying is a sort of algorithmic demonstrating, by which information are created to be obscure. Such displaying approaches measure execution by measurements, for example, accuracy, review, and adjustment, which evaluate various ideas of the recurrence.
AI is the way toward acquisition of a sufficient factual model utilizing watched information to foresee results or classify perceptions in future information. In particular,
administered AI techniques string a model utilizing perceptions on tests where the classes or anticipated estimation of the result of intrigue are now known (a best quality level). The subsequent framework—which is frequently a punished relapse of some structure—is normally applied to new examples to sort or foresee estimations of the result for beforehand inconspicuous perceptions, and its presentation assessed by contrasting anticipated qualities with real qualities for a lot of test tests. In this manner, AI “lives” in the realm of algorithmic demonstrating and ought to be assessed in that capacity. Relapse frameworks created utilizing AI techniques can’t and ought not to be assessed utilizing measures from the universe of information demonstrating. To do so would create wrong evaluations of a model’s presentation for its proposed task, conceivably deceptive clients into off base understanding of the model’s yield.
EHRs give access to an enormous number and assortment of factors that empower top notch grouping and prediction, while AI offers the strategies to deal with the huge bulk of high-dimensional information that are common in a medicinal services setting. Subsequently, the utilization of AI to EHR information investigation is at the bleeding edge of current clinical informatics [5], filling propels in practice of medication and science. We portray the operational and methodological difficulties of utilizing AI in practice and research. Finally, our viewpoint opens doors for AI in medication and applications that have the most noteworthy potential for affecting well-being and social insurance conveyance. This area spreads the extraordinary specific challenges that should be considered in AI systems for restorative administrations endeavors, especially as execution between arranged structures and human pros limits [6]. Failure to intentionally consider these troubles can demolish the authenticity and utility of AI for human administrations. We present levels of leadership of clinical possibilities, sifted through into the going with general groupings: automating clinical endeavors, offering clinical assistance, and developing clinical cut-off points. We close by depicting the open entryways for investigate in AI that have explicit significance in therapeutic administrations: satisfying developments in data sources and instruments, ensuring systems are interpretable, and recognizing incredible depictions
1.2 Need for Change in Healthcare
Much has been created concerning the way medicinal services is changing, with a particular highlight on how incredibly immense measures of data are by and by being routinely accumulated during the ordinary thought of patients. The usage of AI procedures to change these ever-forming measures of data into interventions that can improve steady outcomes seems like it should be an unquestionable method to take. In any case, the field of AI in social insurance is still in its beginning phases. This book, mercifully maintained by the Institution of Engineering and Technology, intends to give a “delineation” of the state of back and forth movement investigate at the interface among AI and restorative administrations. Basically, this is a fragmentary and uneven testing of the state of force analyses, yet then we have expected to give a wide-going preamble to the significance and size of work that is being endeavoured far and wide. In picking material for this modified volume, we have set exceptional complement on AI broadens that are (or are close) achieving improvement in determined outcomes. For certain, reasons, uncovered contrastingly in a bit of the parts that follow, it is an adage that “therapeutic administrations is hard”; there are
stand-out restrictions that exist, and consideration that must be taken, when working with human services data. Regardless, for all of its difficulties, working with restorative administrations data is particularly satisfying, both to the extent the computational troubles that exist and to the extent the yields of exploration having the choice to impact the way social protection is passed on. There are barely any application regions of AI that have such assurance to benefit society as does that of human administrations.
1.3 Opportunities of Machine Learning in Healthcare
Tending to the pecking order of chances in medicinal services makes various open doors for advancement. Importantly, clinical staff and AI scientists frequently have integral aptitudes, and some high-sway issues must be handled by community oriented endeavors. We note a few promising bearings of research, explicitly featuring such issues of information non-stationary, model interpretability, and finding proper portrayals. Regardless of the methodological difficulties of working with EHR information and analysts have however to exploit the universe of EHR-determined factors accessible for prescient displaying, there are many energizing open doors for AI to improve well-being and human services conveyance. frameworks that separate patients into various hazard classifications to advise practice the executives have tremendous potential effect on human services esteem and strategies that can anticipate results for singular patients bring clinical practice one bit nearer to exactness medication [7]. Distinguishing significant expense and high-hazard patients [8] so as to endeavor focused on intercession will turn out to be progressively essential as medicinal services suppliers assume the budgetary danger of handling their patients. AI address has just been utilized to portray and foresee an assortment of well-being dangers. Late work in our gathering utilizing punished strategic relapse to distinguish patients with undiscovered fringe corridor malady and foresee their mortality chance found that such a methodology beats an easier stepwise calculated relapse as far as precision, alignment, and net renaming. Such prescient frameworks have been executed in clinical work on, bringing about progressively proficient and better quality consideration. AI has additionally been applied to medical clinic and practice the board, to smooth out tasks and improve quiet results. For instance, frameworks have been created to anticipate interest for crisis division beds [9] and elective medical procedure case volume [10], to advise emergency clinic staffing choices. As expenses for medicinal services deteriorate at verifiably high costs and the requirement for clinical oversight expands, machine learning for huge scope unstructured information may end up being the answer for this ever-developing issue. A few organizations what’s more, people have set up themselves in the market today with their AI innovation applied to current medication with both unstructured information and organized information. In medicinal services, 50% of the absolute costs originate from 5% of absolute patients; furthermore, the quantity of constant conditions requiring steady, consistent consideration has progressively expanded the nation over. At long last, AI isn’t a panacea, and not everything that can be anticipated will be significant. For instance, we might have the option to precisely anticipate movement from stage 3 to arrange 4 constant renal disappointments. Without viable treatment alternatives—other than kidney transplant and dialysis—the expectation doesn’t do a lot till improve the administration of the sick person. AI can demonstrate to distinguish patients who might be increasingly inclined to repeating diseases what’s more,
help analyse patients. Also, near 90% of crisis room visits are preventable. AI can be utilized to help analyze and direct patients to legitimate treatment all while minimizing expenses by keeping patients out of costly, time escalated crisis care focuses.
1.4 Healthcare Fraud
Social insurance extortion is a serious issue. It is a crime committed by people who make false claims to gain financial gain. In order to identify misrepresentation inside human services framework, the procedure of evaluating is followed by examination. On the off chance that records are cautiously inspected, it is conceivable to recognize suspicious strategy holders and suppliers. In a perfect world, all cases ought to be examined cautiously what’s more, exclusively. In any case, it is difficult to review all cases by any down to earth implies as these structure immense heaps of information including arranging tasks and complex calculation [11]. Besides, it is hard to review specialist co-ops without pieces of information concerning what examiners ought to be searching for. A reasonable methodology is to make short records for investigation and review patients and suppliers dependent on these rundowns. An assortment of expository methods can be utilized to accumulate review short records. Deceitful cases every now and again incorporate with designs that can be seen utilizing prescient models.
1.4.1 Sorts of Fraud in Healthcare
Human services misrepresentation is isolated into four sorts: (Section 1.4.2) clinical specialist co-ops, (Section 1.4.3) clinical asset suppliers, (Section 1.4.4) protection strategy holders, and (Section 1.4.5) insurance strategy suppliers. Figure 1.1 shows the review of fake exercises found in social insurance.
Figure 1.1 Categorization of healthcare fraud.
1.4.2 Clinical Service Providers
Clinical specialist co-ops can be medical clinics, specialists, attendants, radiologists and other research centre specialist organizations, and emergency vehicle organizations. Exercises including Clinical Services are comprised of the following:
✓ Justify certain patient related medical service or procedure or diagnosis which is not relevant medically [12],
✓ Claiming certain services which never took place or claiming extra money by altering the original claims [12],
✓ Charging insurance companies an excess amount i.e., the part of an insurance claim to be paid by the insured [12],
✓ Charging insurance companies something which is not necessary for the patient, for example, by increasing the frequency of the check-ups [12, 13],
✓ charging amount for certain expensive procedures or services which were never performed for the patient [12, 13]
✓ By using illegitimate schemes for which the providers of the healthcare exchange money which alternatively could have been provided by Medicare [13]
1.4.3 Clinical Resource Providers
Clinical asset suppliers include pharmaceutical organizations, clinical gear organizations that gracefully items like wheelchairs, walkers, specific emergency clinic beds what’s more, clinical units. Exercises including Clinical resources provide may include:
✓ Charge insurance companies amount for the equipment which was never procured by modifying or changing the original bill [14].
✓ Resource providers in connivance with the corrupt doctor satisfy their selfish motive [15].
✓ Falsely charging insurance companies for an up-coding item [15].
✓ Making patient available unnecessary or undesirable services which are not required by them.
1.4.4 Protection Policy Holders
Protection strategy holders comprise of people and gatherings who convey protection arrangements, including the two patients and managers of patients. Exercises including Protection Policy Holders may include:
✓ Providing counterfeit eligibility record to take advantage of the benefits [16]
✓ Submitting false claims for the services which were not performed ever before [16]
✓ Availing insurance benefits by using illegitimate or fake card information, and
✓ Exploiting the flaws in the insurance policy to self-benefit.
