Knowledge Engineering In Health Informatics

The "information explosion" in recent decades has made it impossible for practicing physicians (even specialists) to keep up with all the information potentially at their disposal. As a result, it is not surprising that empirical studies have shown that physicians do not always make optimal decisions. Thus, medical expert systems are now available to support - not replace - physicians and healthcare providers in their goal of providing the best possible healthcare to every patient. Knowledge Engineering in Health Informatics is a guide to the creation of such systems. Presenting the core material for courses such as Medical Knowledge Engineering and Expert System Development, it allows non-experts to make diagnostic decisions with the precision and accuracy of medical experts thanks to the help of the computer.

There are several popular books published in Healthcare Computational Informatics like Computational Bioengineering and Bioinformatics (2020), Springer; Health Informatics (2017), Springer; Health Informatics Vision: From Data via Information to Knowledge (2019), IOS Press; Data Analytics in Biomedical Engineering and Healthcare (2020), Elsevier. However, in all these mentioned books, the challenges in Biomedical Imaging are solved in one dimension by use of any specific technology like Image Processing, Machine Learning or Computer Aided Systems. In this book, the book it has been attempted to bring all technologies related to computational analytics together and apply them on Biomedical Imaging.

Medical expert systems led the way in the first generation of expert systems, so it is not surprising that medical expert systems have taken a leading role in the second generation, i.e. deep, expert systems. The aim of this volume is to give an accurate picture of current research on Deep Model approaches directly applicable to the medical field and to present this picture in the context of recent findings. Being a collection of research papers, it is mainly addressed to Artificial Intelligence in Medicine (AIM) researchers, cognitive scientists and medics interested in AIM work. However the volume could provide useful text material for an advanced course in Medical Knowledge Engineering or Medical Informatics.Specifying what characterizes a shallow system is not difficult, namely a knowledge-base of association between data about the problem and (sub)solutions for the problem. By implication a deep system is one which has something over and above a mere associational knowledge-base. Most researchers agree on this point. Where disagreement begins to surface is with regard to what constitutes this something else, this desirable quality, that a deep system should have over an associational system. Deepness is a simple concept to grasp intuitively but it is not so easy to formalise in the context of computer systems; it is a broad, multi-dimensional concept, and this book aims to present different points of view about what constitutes deepness.

HANDBOOK OF INTELLIGENT HEALTHCARE ANALYTICS The book explores the various recent tools and techniques used for deriving knowledge from healthcare data analytics for researchers and practitioners. The power of healthcare data analytics is being increasingly used in the industry. Advanced analytics techniques are used against large data sets to uncover hidden patterns, unknown correlations, market trends, customer preferences, and other useful information. A Handbook on Intelligent Healthcare Analytics covers both the theory and application of the tools, techniques, and algorithms for use in big data in healthcare and clinical research. It provides the most recent research findings to derive knowledge using big data analytics, which helps to analyze huge amounts of real-time healthcare data, the analysis of which can provide further insights in terms of procedural, technical, medical, and other types of improvements in healthcare. In addition, the reader will find in this Handbook: Innovative hybrid machine learning and deep learning techniques applied in various healthcare data sets, as well as various kinds of machine learning algorithms existing such as supervised, unsupervised, semi-supervised, reinforcement learning, and guides how readers can implement the Python environment for machine learning; An exploration of predictive analytics in healthcare; The various challenges for smart healthcare, including privacy, confidentiality, authenticity, loss of information, attacks, etc., that create a new burden for providers to maintain compliance with healthcare data security. In addition, this book also explores various sources of personalized healthcare data and the commercial platforms for healthcare data analytics. Audience Healthcare professionals, researchers, and practitioners who wish to figure out the core concepts of smart healthcare applications and the innovative methods and technologies used in healthcare will all benefit from this book.

Business intelligence (BI) tools are capable of working with healthcare data in an efficient manner to generate real-time information and knowledge relevant to the success of healthcare organizations. Further, BI tools benefit healthcare professionals making critical decisions within hospitals, clinics, and physicians’ offices. Applying Business Intelligence to Clinical and Healthcare Organizations presents new solutions for data analysis within the healthcare sector in order to improve the quality of medical care and patient quality of life. Business intelligence models and techniques are explored and their benefits for the healthcare sector exposed in this timely research-based publication comprised of chapters written by professionals and researchers from around the world. Hospital administrators, healthcare professionals, biomedical engineers, informatics engineers, and students in graduate-level healthcare management programs will find this publication essential to their professional development and research needs.

his volume of the series Lecture Notes in Medical Informatics contains the T proceedings of the Workshop on System Engineering in Medicine, which was held in Maastricht, The Netherlands, 16-18 March 1989. This workshop was sponsored by the EC under the framework of the Medical and Health Research Programme. The aim of the workshop was to assess whether there was sufficient support in the Medical Informatics community in the EC to establish a concerted action. This proceedings contain papers of the presentations given at the workshop. These presentations were centred around three themes: • Methods and Tools • Applications in the domains of chronic care and critical care • Evaluation of decision support systems The papers were prepared after the workshop and therefore we were able to include the relevant parts of the discussions which were related to the presentations. As a result of the discussions during the workshop, a proposal was prepared for the establishment of a concerted action, specifically addressing the development of guidelines for the evaluation of medical decision aids. This proposal was granted early 1990 under the same Medical and Health Research programme of the EC. Over 40 institutes are participating in this concerted action. It have been the outstanding presentations and the open discussions at the workshop that have been the starting point of this concerted action. The papers in this proceedings formed a starting point for the discussions in the meetings of the concerted action.

Knowledge Modelling and Big Data Analytics in Healthcare: Advances and Applications focuses on automated analytical techniques for healthcare applications used to extract knowledge from a vast amount of data. It brings together a variety of different aspects of the healthcare system and aids in the decision-making processes for healthcare professionals. The editors connect four contemporary areas of research rarely brought together in one book: artificial intelligence, big data analytics, knowledge modelling, and healthcare. They present state-of-the-art research from the healthcare sector, including research on medical imaging, healthcare analysis, and the applications of artificial intelligence in drug discovery. This book is intended for data scientists, academicians, and industry professionals in the healthcare sector.

Biomedical and Health Informatics is an important field that brings tremendous opportunities and helps address challenges due to an abundance of available biomedical data. This book examines and demonstrates state-of-the-art approaches for IoT and Machine Learning based biomedical and health related applications. This book aims to provide computational methods for accumulating, updating and changing knowledge in 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 the future IoT has the impending capability to change the way we work and live. These computing methods also play a significant role in design and optimization in diverse engineering disciplines. With the influence and the development of the 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 IoTsystems generate a more intelligent and robust system providing a human interpretable, low-cost, and approximate solution. Intelligent IoT systems have demonstrated great performance to a variety of areas including big data analytics, time series, biomedical and health informatics. This book will be very beneficial for the new researchers and practitioners working in the biomedical and healthcare fields to quickly know the best performing methods. It will also be suitable for a wide range of readers who may not be scientists but who are also interested in the practice of such areas as medical image retrieval, brain image segmentation, among others. • Discusses deep learning, IoT, machine learning, and biomedical data analysis with broad coverage of basic scientific applications • Presents deep learning and the tremendous improvement in accuracy, robustness, and cross- language generalizability it has over conventional approaches • Discusses various techniques of IoT systems for healthcare data analytics • Provides state-of-the-art methods of deep learning, machine learning and IoT in biomedical and health informatics • Focuses more on the application of algorithms in various real life biomedical and engineering problems