Explore the theory and practical applications of artificial intelligence (AI) and machine learning in healthcare. This book offers a guided tour of machine learning algorithms, architecture design, and applications of learning in healthcare and big data challenges. You’ll discover the ethical implications of healthcare data analytics and the future of AI in population and patient health optimization. You’ll also create a machine learning model, evaluate performance and operationalize its outcomes within your organization. Machine Learning and AI for Healthcare provides techniques on how to apply machine learning within your organization and evaluate the efficacy, suitability, and efficiency of AI applications. These are illustrated through leading case studies, including how chronic disease is being redefined through patient-led data learning and the Internet of Things. What You'll Learn Gain a deeper understanding of key machine learning algorithms and their use and implementation within wider healthcare Implement machine learning systems, such as speech recognition and enhanced deep learning/AI Select learning methods/algorithms and tuning for use in healthcare Recognize and prepare for the future of artificial intelligence in healthcare through best practices, feedback loops and intelligent agents Who This Book Is For Health care professionals interested in how machine learning can be used to develop health intelligence – with the aim of improving patient health, population health and facilitating significant care-payer cost savings.
About the book:? Do you know what AI is doing to improve our health and wellbeing?? Does this new technology concern you, or impress you?? Do you want to know more about the future of AI in healthcare?Technology continues to advance at a pace that can seem bewildering. Nowhere else is it moving faster than in the health sector, where ?AI is now being used to improve millions of lives?.In this book, ? Artificial Intelligence in Healthcare: AI, Machine Learning, and Deep and Intelligent Medicine Simplified for Everyone ?, you can discover the great improvements that AI is making, with chapters covering:? The current applications and future of AI in healthcare and all major medical specialties? The benefits and risks weighed up? The ethics involved? Machine learning and data science simplified? AI's role in medical research and education, health insurance, drug discovery, electronic health records, and the fight against COVID-19? The roles that major corporations and start-up companies are playing? The implementation of AI in clinical practice? And lots more?Quite simply the most authoritative text on the subject, Artificial Intelligence in Healthcare - 3rd Edition, is an absorbing and compelling read for anyone who wants to know more. It is packed with more updated information than any other book currently available, written in easy-to-understand language, and accessible to all.
Artificial Intelligence and Machine Learning in Healthcare
Create real-world machine learning solutions using NumPy, pandas, matplotlib, and scikit-learn Key Features Develop a range of healthcare analytics projects using real-world datasets Implement key machine learning algorithms using a range of libraries from the Python ecosystem Accomplish intermediate-to-complex tasks by building smart AI applications using neural network methodologies Book Description Machine Learning (ML) has changed the way organizations and individuals use data to improve the efficiency of a system. ML algorithms allow strategists to deal with a variety of structured, unstructured, and semi-structured data. Machine Learning for Healthcare Analytics Projects is packed with new approaches and methodologies for creating powerful solutions for healthcare analytics. This book will teach you how to implement key machine learning algorithms and walk you through their use cases by employing a range of libraries from the Python ecosystem. You will build five end-to-end projects to evaluate the efficiency of Artificial Intelligence (AI) applications for carrying out simple-to-complex healthcare analytics tasks. With each project, you will gain new insights, which will then help you handle healthcare data efficiently. As you make your way through the book, you will use ML to detect cancer in a set of patients using support vector machines (SVMs) and k-Nearest neighbors (KNN) models. In the final chapters, you will create a deep neural network in Keras to predict the onset of diabetes in a huge dataset of patients. You will also learn how to predict heart diseases using neural networks. By the end of this book, you will have learned how to address long-standing challenges, provide specialized solutions for how to deal with them, and carry out a range of cognitive tasks in the healthcare domain. What you will learn Explore super imaging and natural language processing (NLP) to classify DNA sequencing Detect cancer based on the cell information provided to the SVM Apply supervised learning techniques to diagnose autism spectrum disorder (ASD) Implement a deep learning grid and deep neural networks for detecting diabetes Analyze data from blood pressure, heart rate, and cholesterol level tests using neural networks Use ML algorithms to detect autistic disorders Who this book is for Machine Learning for Healthcare Analytics Projects is for data scientists, machine learning engineers, and healthcare professionals who want to implement machine learning algorithms to build smart AI applications. Basic knowledge of Python or any programming language is expected to get the most from this book.
"Artificial intelligence (AI) and machine learning (ML) in healthcare comprise a rapidly expanding and very promising field. The marriage of technology and health has the potential to empower medical professionals and patients alike, while drastically cutting the cost of healthcare. Using a measured no-hype approach, this video will help technology entrepreneurs, product managers, and health care executives understand what can be done with AI and ML in healthcare today and what concepts are most crucial to producing valuable applications in the near future."--Resource description page.
The rediscovery of the potential of artificial intelligence (AI) to improve healthcare delivery and patient outcomes has led to an increasing application of AI techniques such as deep learning, computer vision, natural language processing, and robotics in the healthcare domain. Many governments and health authorities have prioritized the application of AI in the delivery of healthcare. Also, technological giants and leading universities have established teams dedicated to the application of AI in medicine. These trends will mean an expanded role for AI in the provision of healthcare. Yet, there is an incomplete understanding of what AI is and its potential for use in healthcare. This book discusses the different types of AI applicable to healthcare and their application in medicine, population health, genomics, healthcare administration, and delivery. Readers, especially healthcare professionals and managers, will find the book useful to understand the different types of AI and how they are relevant to healthcare delivery. The book provides examples of AI being applied in medicine, population health, genomics, healthcare administration, and delivery and how they can commence applying AI in their health services. Researchers and technology professionals will also find the book useful to note current trends in the application of AI in healthcare and initiate their own projects to enable the application of AI in healthcare/medical domains.
Artificial Intelligence (AI) in Healthcare is more than a comprehensive introduction to artificial intelligence as a tool in the generation and analysis of healthcare data. The book is split into two sections where the first section describes the current healthcare challenges and the rise of AI in this arena. The ten following chapters are written by specialists in each area, covering the whole healthcare ecosystem. First, the AI applications in drug design and drug development are presented followed by its applications in the field of cancer diagnostics, treatment and medical imaging. Subsequently, the application of AI in medical devices and surgery are covered as well as remote patient monitoring. Finally, the book dives into the topics of security, privacy, information sharing, health insurances and legal aspects of AI in healthcare. Highlights different data techniques in healthcare data analysis, including machine learning and data mining Illustrates different applications and challenges across the design, implementation and management of intelligent systems and healthcare data networks Includes applications and case studies across all areas of AI in healthcare data
Applications of Artificial Intelligence and Machine Learning in Healthcare
Artificial Intelligence (AI) is becoming a ubiquitous term that is used in many fields of research or the popular culture. Among these fields that was affected by this hype is the healthcare sector. Along with its subdomain, Machine Learning (ML), they established an environment of interest in the promises of machines versus humans capabilities. Though artificial intelligence applications in healthcare such as interpreting ECGs could date back to the mid of the twentieth century, the promises of AI still at its beginning when it comes to new breakthroughs. This is due to the transformation into a digital world and new advancements in the processing capabilities. Computer vision has contributed the most to the healthcare sector where it can leverage doctors and practitioners with automated classification and annotations as a preparing step. This kind of mechanism is the best suited for applications of AI in healthcare. However, the amount of data in other forms such as textual or lab results is exceeding the force power. While a solution could be to use machines to learn and propose solutions, the results could be catastrophic and human lives are on stake. So, explainable AI could be beneficial where it analyzes and makes predictions that can be trusted by the users. The study here is conducted on cardiovascular patients dataset to predict the presence or absence of the disease. Classifications techniques used include Nave Bayes, Logistic Regression, Decision Trees, Support Vector Machines, and Artificial Neural Networks. The Logistic regression model achieved the best Area under the curve. Moreover, an extension of the previous studies discussed is conducted to explain the model and to show how models of AI can be trusted and not used as black-boxes.
Advancement of Artificial Intelligence in Healthcare Engineering
"This book explores the possible applications of machine learning, deep learning, soft computing, and evolutionary computing techniques in the design, implementation, and optimization of challenging healthcare engineering solutions"--
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.