Introduction To Computational Health Informatics

This class-tested textbook is designed for a semester-long graduate or senior undergraduate course on Computational Health Informatics. The focus of the book is on computational techniques that are widely used in health data analysis and health informatics and it integrates computer science and clinical perspectives. This book prepares computer science students for careers in computational health informatics and medical data analysis. Features Integrates computer science and clinical perspectives Describes various statistical and artificial intelligence techniques, including machine learning techniques such as clustering of temporal data, regression analysis, neural networks, HMM, decision trees, SVM, and data mining, all of which are techniques used widely used in health-data analysis Describes computational techniques such as multidimensional and multimedia data representation and retrieval, ontology, patient-data deidentification, temporal data analysis, heterogeneous databases, medical image analysis and transmission, biosignal analysis, pervasive healthcare, automated text-analysis, health-vocabulary knowledgebases and medical information-exchange Includes bioinformatics and pharmacokinetics techniques and their applications to vaccine and drug development

This second, extensively revised and updated edition of Health Informatics: An Overview includes new topics which address contemporary issues and challenges and shift the focus on the health problem space towards a computer perspective.

An Introduction to Healthcare Informatics: Building Data-Driven Tools bridges the gap between the current healthcare IT landscape and cutting edge technologies in data science, cloud infrastructure, application development and even artificial intelligence. Information technology encompasses several rapidly evolving areas, however healthcare as a field suffers from a relatively archaic technology landscape and a lack of curriculum to effectively train its millions of practitioners in the skills they need to utilize data and related tools. The book discusses topics such as data access, data analysis, big data current landscape and application architecture. Additionally, it encompasses a discussion on the future developments in the field. This book provides physicians, nurses and health scientists with the concepts and skills necessary to work with analysts and IT professionals and even perform analysis and application architecture themselves. Presents case-based learning relevant to healthcare, bringing each concept accompanied by an example which becomes critical when explaining the function of SQL, databases, basic models etc. Provides a roadmap for implementing modern technologies and design patters in a healthcare setting, helping the reader to understand both the archaic enterprise systems that often exist in hospitals as well as emerging tools and how they can be used together Explains healthcare-specific stakeholders and the management of analytical projects within healthcare, allowing healthcare practitioners to successfully navigate the political and bureaucratic challenges to implementation Brings diagrams for each example and technology describing how they operate individually as well as how they fit into a larger reference architecture built upon throughout the book

COMPUTATIONAL INTELLIGENCE and HEALTHCARE INFORMATICS The book provides the state-of-the-art innovation, research, design, and implements methodological and algorithmic solutions to data processing problems, designing and analysing evolving trends in health informatics, intelligent disease prediction, and computer-aided diagnosis. Computational intelligence (CI) refers to the ability of computers to accomplish tasks that are normally completed by intelligent beings such as humans and animals. With the rapid advance of technology, artificial intelligence (AI) techniques are being effectively used in the fields of health to improve the efficiency of treatments, avoid the risk of false diagnoses, make therapeutic decisions, and predict the outcome in many clinical scenarios. Modern health treatments are faced with the challenge of acquiring, analyzing and applying the large amount of knowledge necessary to solve complex problems. Computational intelligence in healthcare mainly uses computer techniques to perform clinical diagnoses and suggest treatments. In the present scenario of computing, CI tools present adaptive mechanisms that permit the understanding of data in difficult and changing environments. The desired results of CI technologies profit medical fields by assembling patients with the same types of diseases or fitness problems so that healthcare facilities can provide effectual treatments. This book starts with the fundamentals of computer intelligence and the techniques and procedures associated with it. Contained in this book are state-of-the-art methods of computational intelligence and other allied techniques used in the healthcare system, as well as advances in different CI methods that will confront the problem of effective data analysis and storage faced by healthcare institutions. The objective of this book is to provide researchers with a platform encompassing state-of-the-art innovations; research and design; implementation of methodological and algorithmic solutions to data processing problems; and the design and analysis of evolving trends in health informatics, intelligent disease prediction and computer-aided diagnosis. Audience The book is of interest to artificial intelligence and biomedical scientists, researchers, engineers and students in various settings such as pharmaceutical & biotechnology companies, virtual assistants developing companies, medical imaging & diagnostics centers, wearable device designers, healthcare assistance robot manufacturers, precision medicine testers, hospital management, and researchers working in healthcare system.

The first resource of its kind, Introduction to Health Informatics examined the effects of health informatics on healthcare practitioners, patients, and policies from a distinctly Canadian perspective. This second edition has been thoroughly updated to reflect current trends and innovations in health informatics and includes new figures, charts, tables, and web links. In this text, author Christo El Morr presents the subject of health informatics in an accessible, concise way, breaking the topic down into 12 chapters divided into 3 sections. Each chapter includes objectives, key terms, which are defined in a full glossary at the end of the text, and a “Test Your Understanding” section for student review. The second edition also features 15% brand new content, with a full chapter on analytics, machine learning, and AI for health, as well as information on virtual care, mHealth apps, COVID-19 responses, adoption of EHR across provinces, clinical informatics, and precisions medicine. Packed with pedagogical features and updated instructor supplements, this text is a vital resource for students, instructors, and practitioners in health informatics, health management, and health policy. FEATURES: - Takes a uniquely Canadian perspective on health informatics - Contains 15 percent new content on topics such as virtual care, mHealth apps, COVID-19 responses, adoption of EHR across provinces, clinical informatics, and precisions medicine - Updated instructor supplements, including PowerPoint slides and a test bank

This essential text provides a readable yet sophisticated overview of the basic concepts of information technologies as they apply in healthcare. Spanning areas as diverse as the electronic medical record, searching, protocols, and communications as well as the Internet, Enrico Coiera has succeeded in making this vast and complex area accessible an

This book provides a comprehensive overview of different biomedical data types, including both clinical and genomic data. Thorough explanations enable readers to explore key topics ranging from electrocardiograms to Big Data health mining and EEG analysis techniques. Each chapter offers a summary of the field and a sample analysis. Also covered are telehealth infrastructure, healthcare information association rules, methods for mass spectrometry imaging, environmental biodiversity, and the global nonlinear fitness function for protein structures. Diseases are addressed in chapters on functional annotation of lncRNAs in human disease, metabolomics characterization of human diseases, disease risk factors using SNP data and Bayesian methods, and imaging informatics for diagnostic imaging marker selection. With the exploding accumulation of Electronic Health Records (EHRs), there is an urgent need for computer-aided analysis of heterogeneous biomedical datasets. Biomedical data is notorious for its diversified scales, dimensions, and volumes, and requires interdisciplinary technologies for visual illustration and digital characterization. Various computer programs and servers have been developed for these purposes by both theoreticians and engineers. This book is an essential reference for investigating the tools available for analyzing heterogeneous biomedical data. It is designed for professionals, researchers, and practitioners in biomedical engineering, diagnostics, medical electronics, and related industries.

Smart Computational Intelligence in Biomedical and Health Informatics presents state-of-art innovations, research, design, and implementation of methodological and algorithmic solutions to data processing problems, designing, including analysis of evolving trends in health informatics and computer-aided diagnosis. Further, it describes practical, applications-led research on the use of methods and devices in clinical diagnosis, disease prevention, patient monitoring and management. It covers simulation and modeling, measurement and control, analysis, information extraction and monitoring of physiological data in clinical medicine and the biological sciences. Covers evolutionary approaches to solve optimization problems in biomedical engineering. Discusses IoT, Cloud computing, data analytics in healthcare informatics. Provides computational intelligence-based solution for diagnosis of diseases. Reviews modelling and simulations in designing of biomedical equipment. Promotes machine learning based approaches to improvements in biomedical engineering problems. This book is aimed at researchers, graduate students in healthcare, biomedical engineering, health informatics, computational intelligence, and machine learning.