When considering the idea of using machine learning in healthcare, it is a Herculean task to present the entire gamut of information in the field of intelligent systems. It is, therefore the objective of this book to keep the presentation narrow and intensive. This approach 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.

This unique book introduces a variety of techniques designed to represent, enhance and empower multi-disciplinary and multi-institutional machine learning research in healthcare informatics. Providing a unique compendium of current and emerging machine learning paradigms for healthcare informatics, it reflects the diversity, complexity, and the depth and breadth of this multi-disciplinary area. Further, it describes techniques for applying machine learning within organizations and explains how to evaluate the efficacy, suitability, and efficiency of such applications. Featuring illustrative case studies, including how chronic disease is being redefined through patient-led data learning, the book offers a guided tour of machine learning algorithms, architecture design, and applications of learning in healthcare challenges.

The study of healthcare data collection, transmission, processing, storage, and retrieval is called healthcare informatics. This area of study is crucial for preventing sickness, detecting diseases early, diagnosing them early, and treating them early. In the field of healthcare informatics, the sole data that is deemed trustworthy pertains to diseases, patient records, and the computational processes needed to decipher this data. In the past 20 years, traditional medical practices in the US have poured a lot of money on cutting-edge computing and technology infrastructure to help them better serve patients, doctors, and academics. Much effort has gone into improving the quality of medical care that can be delivered using these methods. The driving force behind all of these endeavors was a desire to provide patients with healthcare that was not only affordable and of high quality, but also entirely anxiety-free. Thanks to these initiatives, the value of computational tools for facilitating prescriptions and referrals, establishing and maintaining EHR, and advancing digital medical imaging technology has been increasingly apparent. The installation and administration of electronic health records (EHR) can also be facilitated by these instruments. Clinical trials have demonstrated that computerized physician order entry (CPOE) has the potential to enhance patient care while decreasing medication errors and side effects. By utilizing CPOE, doctors may quickly access relevant patient data without leaving the screen where they are inputting prescriptions. The patient's medical history alerts the treating physician to any potential adverse reactions in advance. Another perk of CPOE is that it lets doctors track their orders as they progress through the system. This provides an additional tool for doctors to assess prescription issues and revise them to remove human error as a potential cause. A logical outgrowth of AI research, machine learning emerged with the field's maturation. Researchers and doctors often turn to machine learning when faced with challenging statistical computations. When people talk about healthcare informatics, they usually imply the study of how to use machine learning in conjunction with healthcare data to find important trends in healthcare. That is why healthcare informatics is all on finding patterns in data so you can learn more. The broad usage of electronic health records (EHRs) has helped bring down the cost of medical treatment by making it easier for hospitals to access and exchange their patients' medical information. Cuts to overhead and elimination of superfluous health exams likely contributed to this price drop. Nevertheless, with the current state of EHR administration, it is difficult to collect and analyze clinical data for trends and patterns across distinct populations. This is because there is now a great deal of uncertainty around the administration of EHR systems. The American Recovery and Reinvestment Act (ARRA) of 20091 and similar programs have made great strides in the direction of standardizing the digitalization of medical records. This makes the possibility of building massive medical databases a real possibility. When data is retrieved from these massive archives, machine learning may be employed to create forecasts and comprehend patterns in other domains. Finding strategies to avoid the computational difficulties that are preventing the distribution, sharing, and standardization of electronic health records (EHRs) is the fundamental objective of research that is being conducted in this area. Because these databases contain sensitive information on patients, the objective is to create openaccess databases that are not just secure but also resistant to a wide variety of cyberthreats. This is because the databases contain sensitive information about individual patients. The regional medical databases that are given below are some samples of some of the most well-known databases in the country: Before these vast data repositories of medical information can be developed, there are a number of obstacles that need to be overcome, as will be illustrated in the following sections; substantial expenditures in research and computer resources are required in order to handle these challenges. In order to resolve these challenges, it is necessary to have a significant amount of money. For instance, in order to integrate newly developed technologies for medical devices and the data that they generate, it will be necessary to manage data structures that are always evolving in order to accommodate these new technologies. It is inevitable that this will occur due to the fact that it will be essential to adapt to the new technology.

This book brings together a blend of different areas of machine learning and recent advances in the area. From the use of ML in healthcare to security, this book encompasses several areas related to ML while keeping a check on traditional ML algorithms. Machine Learning in Healthcare and Security: Advances, Obstacles, and Solutions describes the predictive analysis and forecasting techniques in different emerging and classical areas using the approaches of ML and AI. It discusses the application of ML and AI in medical diagnostic systems and deals with the security prevention aspects of ML and how it can be used to tackle various emerging security issues. This book also focuses on NLP and understanding the techniques, obstacles, and possible solutions. This is a valuable reference resource for researchers and postgraduate students in healthcare systems engineering, computer science, cyber-security, information technology, and applied mathematics.

This book constitutes the proceedings of First International Workshop, TML4H 2023, held virtually, in May 2023. The 16 full papers included in this volume were carefully reviewed and selected from 30 submissions. The goal of this workshop is to bring together experts from academia, clinic, and industry with an insightful vision of promoting trustworthy machine learning in healthcare in terms of scalability, accountability, and explainability.

Machine Learning and Deep Learning in Medical Data Analytics and Healthcare Applications introduces and explores a variety of schemes designed to empower, enhance, and represent multi-institutional and multi-disciplinary machine learning (ML) and deep learning (DL) research in healthcare paradigms. Serving as a unique compendium of existing and emerging ML/DL paradigms for the healthcare sector, this book demonstrates the depth, breadth, complexity, and diversity of this multi-disciplinary area. It provides a comprehensive overview of ML/DL algorithms and explores the related use cases in enterprises such as computer-aided medical diagnostics, drug discovery and development, medical imaging, automation, robotic surgery, electronic smart records creation, outbreak prediction, medical image analysis, and radiation treatments. This book aims to endow different communities with the innovative advances in theory, analytical results, case studies, numerical simulation, modeling, and computational structuring in the field of ML/DL models for healthcare applications. It will reveal different dimensions of ML/DL applications and will illustrate their use in the solution of assorted real-world biomedical and healthcare problems. Features: Covers the fundamentals of ML and DL in the context of healthcare applications Discusses various data collection approaches from various sources and how to use them in ML/DL models Integrates several aspects of AI-based computational intelligence such as ML and DL from diversified perspectives which describe recent research trends and advanced topics in the field Explores the current and future impacts of pandemics and risk mitigation in healthcare with advanced analytics Emphasizes feature selection as an important step in any accurate model simulation where ML/DL methods are used to help train the system and extract the positive solution implicitly This book is a valuable source of information for researchers, scientists, healthcare professionals, programmers, and graduate-level students interested in understanding the applications of ML/DL in healthcare scenarios. Dr. Om Prakash Jena is an Assistant Professor in the Department of Computer Science, Ravenshaw University, Cuttack, Odisha, India. Dr. Bharat Bhushan is an Assistant Professor of Department of Computer Science and Engineering (CSE) at the School of Engineering and Technology, Sharda University, Greater Noida, India. Dr. Utku Kose is an Associate Professor in Suleyman Demirel University, Turkey.

This book is about the use of artificial intelligence (AI) and machine learning in healthcare. AI and related technologies are increasingly prevalent in business and society and are beginning to be applied to healthcare. These technologies have the potential to transform many aspects of patient care, as well as administrative processes within provider, payer, and pharmaceutical organizations. There are already a number of research studies suggesting that AI can perform as well as or better than humans at key healthcare tasks, such as diagnosing disease. Today, algorithms are already outperforming radiologists at spotting malignant tumors and guiding researchers in how to construct cohorts for costly clinical trials. However, for a variety of reasons, the authors believe that it will be many years before AI replaces humans for broad medical process domains. Through this book, the authors describe both the potential that AI offers to automate aspects of care and some of the barriers to rapid implementation of AI in healthcare.

This book presents a variety of techniques designed to enhance and empower multi-disciplinary and multi-institutional machine learning research in healthcare informatics. It is intended to provide a unique compendium of current and emerging machine learning paradigms for healthcare informatics, reflecting the diversity, complexity, and depth and breadth of this multi-disciplinary area.

Prediction in Medicine: The Impact of Machine Learning on Healthcare explores the transformative power of advanced data analytics and machine learning in healthcare. This comprehensive guide covers predictive analysis, leveraging electronic health records (EHRs) and wearable devices to optimize patient care and healthcare planning. Key topics include disease diagnosis, risk assessment, and precision medicine advancements in cardiovascular health and hypertension management. The book also addresses challenges in interpreting clinical data and navigating ethical considerations. It examines the role of AI in healthcare emergencies and infectious disease management, highlighting the integration of diverse data sources like medical imaging and genomic data. Prediction in Medicine is essential for students, researchers, healthcare professionals, and general readers interested in the future of healthcare and technological innovation.