Author: Sumeet Dua
Publisher: Springer Science & Business Media
Release Date: 2013-12-09
The book is a unique effort to represent a variety of techniques designed to represent, enhance, and empower multi-disciplinary and multi-institutional machine learning research in healthcare informatics. The book provides a unique compendium of current and emerging machine learning paradigms for healthcare informatics and reflects the diversity, complexity and the depth and breath of this multi-disciplinary area. The integrated, panoramic view of data and machine learning techniques can provide an opportunity for novel clinical insights and discoveries.
With an evolutionary advancement of Machine Learning (ML) algorithms, a rapid increase of data volumes and a significant improvement of computation powers, machine learning becomes hot in different applications. However, because of the nature of “black-box” in ML methods, ML still needs to be interpreted to link human and machine learning for transparency and user acceptance of delivered solutions. This edited book addresses such links from the perspectives of visualisation, explanation, trustworthiness and transparency. The book establishes the link between human and machine learning by exploring transparency in machine learning, visual explanation of ML processes, algorithmic explanation of ML models, human cognitive responses in ML-based decision making, human evaluation of machine learning and domain knowledge in transparent ML applications. This is the first book of its kind to systematically understand the current active research activities and outcomes related to human and machine learning. The book will not only inspire researchers to passionately develop new algorithms incorporating human for human-centred ML algorithms, resulting in the overall advancement of ML, but also help ML practitioners proactively use ML outputs for informative and trustworthy decision making. This book is intended for researchers and practitioners involved with machine learning and its applications. The book will especially benefit researchers in areas like artificial intelligence, decision support systems and human-computer interaction.
As storage and collection technology has become cheaper and more precise, companies and individuals are eager to extract relevant information from large data sets. This book focuses on the tools of machine learning and statistics in a practical manner, with lots of case studies specific to the challenges of working with healthcare data. By exploring each problem in depth, you’ll build your intuitive understanding of machine learning without requiring a strong background in advanced mathematics. You’ll be able to recognize when your problems match traditional problems closely, and apply classical tools from statistics to your problems, while working within the legal bounds of the US healthcare system.
Author: David D. Luxton
Publisher: Academic Press
Release Date: 2015-09-10
Artificial Intelligence in Behavioral and Mental Health Care summarizes recent advances in artificial intelligence as it applies to mental health clinical practice. Each chapter provides a technical description of the advance, review of application in clinical practice, and empirical data on clinical efficacy. In addition, each chapter includes a discussion of practical issues in clinical settings, ethical considerations, and limitations of use. The book encompasses AI based advances in decision-making, in assessment and treatment, in providing education to clients, robot assisted task completion, and the use of AI for research and data gathering. This book will be of use to mental health practitioners interested in learning about, or incorporating AI advances into their practice and for researchers interested in a comprehensive review of these advances in one source. Summarizes AI advances for use in mental health practice Includes advances in AI based decision-making and consultation Describes AI applications for assessment and treatment Details AI advances in robots for clinical settings Provides empirical data on clinical efficacy Explores practical issues of use in clinical settings
Author: Dawn E. Holmes
Release Date: 2017-10-18
This book presents authoritative recent research on Biomedical Informatics, bringing together contributions from some of the most respected researchers in this field. Biomedical Informatics represents a growing area of interest and innovation in the management of health-related data, and is essential to the development of focused computational models. Outlining the direction of current research, the book will be of considerable interest to theoreticians and application scientists alike. Further, as all chapters are self-contained, it also provides a valuable sourcebook for graduate students.
This book constitutes the refereed proceedings of the 8th International Conference on Knowledge Science, Engineering and Management, KSEM 2015, held in Chongqing, China, in October 2015. The 57 revised full papers presented together with 22 short papers and 5 keynotes were carefully selected and reviewed from 247 submissions. The papers are organized in topical sections on formal reasoning and ontologies; knowledge management and concept analysis; knowledge discovery and recognition methods; text mining and analysis; recommendation algorithms and systems; machine learning algorithms; detection methods and analysis; classification and clustering; mobile data analytics and knowledge management; bioinformatics and computational biology; and evidence theory and its application.
This book contains an interesting and state-of the art collection of chapters presenting several examples of attempts to developing modern tools utilizing computational intelligence in different real life problems encountered by humans. Reasoning, prediction, modeling, optimization, decision making, etc. need modern, soft and intelligent algorithms, methods and methodologies to solve, in the efficient ways, problems appearing in human activity. The contents of the book is divided into two parts. Part I, consisting of four chapters, is devoted to selected links of computational intelligence, medicine, health care and biomechanics. Several problems are considered: estimation of healthcare system reliability, classification of ultrasound thyroid images, application of fuzzy logic to measure weight status and central fatness, and deriving kinematics directly from video records. Part II, also consisting of four chapters, is devoted to selected links of computational intelligence and biology. The common denominator of three chapters is Physarum polycephalum, one-cell organisms able to build complex networks for solving different computational tasks. One chapter focuses on a novel device, the memristor, that has possible uses both in the creation of hardware neural nets for artificial intelligence and as the connection between a hardware neural net and a living neuronal cell network in the treatment and monitoring of neurological disease. This book is intended for a wide audience of readers who are interested in various aspects of computational intelligence.
This volume comprises of 21 selected chapters, including two overview chapters devoted to abdominal imaging in clinical applications supported computer aided diagnosis approaches as well as different techniques for solving the pectoral muscle extraction problem in the preprocessing part of the CAD systems for detecting breast cancer in its early stage using digital mammograms. The aim of this book is to stimulate further research in medical imaging applications based algorithmic and computer based approaches and utilize them in real-world clinical applications. The book is divided into four parts, Part-I: Clinical Applications of Medical Imaging, Part-II: Classification and clustering, Part-III: Computer Aided Diagnosis (CAD) Tools and Case Studies and Part-IV: Bio-inspiring based Computer Aided diagnosis techniques.
Healthcare transformation requires us to continually look at new and better ways to manage insights – both within and outside the organization today. Increasingly, the ability to glean and operationalize new insights efficiently as a byproduct of an organization’s day-to-day operations is becoming vital to hospitals and health systems ability to survive and prosper. One of the long-standing challenges in healthcare informatics has been the ability to deal with the sheer variety and volume of disparate healthcare data and the increasing need to derive veracity and value out of it. Demystifying Big Data and Machine Learning for Healthcare investigates how healthcare organizations can leverage this tapestry of big data to discover new business value, use cases, and knowledge as well as how big data can be woven into pre-existing business intelligence and analytics efforts. This book focuses on teaching you how to: Develop skills needed to identify and demolish big-data myths Become an expert in separating hype from reality Understand the V’s that matter in healthcare and why Harmonize the 4 C’s across little and big data Choose data fi delity over data quality Learn how to apply the NRF Framework Master applied machine learning for healthcare Conduct a guided tour of learning algorithms Recognize and be prepared for the future of artificial intelligence in healthcare via best practices, feedback loops, and contextually intelligent agents (CIAs) The variety of data in healthcare spans multiple business workflows, formats (structured, un-, and semi-structured), integration at point of care/need, and integration with existing knowledge. In order to deal with these realities, the authors propose new approaches to creating a knowledge-driven learning organization-based on new and existing strategies, methods and technologies. This book will address the long-standing challenges in healthcare informatics and provide pragmatic recommendations on how to deal with them.
Author: Donna L. Hudson
Publisher: Wiley-IEEE Press
Release Date: 2000
Biomedical/Electrical Engineering Neural Networks and Artificial Intelligence for Biomedical Engineering Using examples drawn from biomedicine and biomedical engineering, this reference text provides comprehensive coverage of all the major techniques currently available to build computer-assisted decision support systems. You will find practical solutions for biomedicine based on current theory and applications of neural networks, artificial intelligence, and other methods for the development of decision-making aids, including hybrid systems. Neural Networks and Artificial Intelligence for Biomedical Engineering offers students and scientists of biomedical engineering, biomedical informatics, and medical artificial intelligence a deeper understanding of the powerful techniques currently used with a wide range of biomedical applications. Highlighted topics include: Types of neural networks and neural network algorithms Knowledge-based representation and acquisition Reasoning methodologies and searching strategies Chaotic analysis of biomedical time series Genetic algorithms Probability-based systems and fuzzy systems Case study and MATLAB(R) exercises Evaluation and validation of decision support aids
Author: Richard E. Neapolitan
Publisher: CRC Press
Release Date: 2018-03-12
The first edition of this popular textbook, Contemporary Artificial Intelligence, provided an accessible and student friendly introduction to AI. This fully revised and expanded update, Artificial Intelligence: With an Introduction to Machine Learning, Second Edition, retains the same accessibility and problem-solving approach, while providing new material and methods. The book is divided into five sections that focus on the most useful techniques that have emerged from AI. The first section of the book covers logic-based methods, while the second section focuses on probability-based methods. Emergent intelligence is featured in the third section and explores evolutionary computation and methods based on swarm intelligence. The newest section comes next and provides a detailed overview of neural networks and deep learning. The final section of the book focuses on natural language understanding. Suitable for undergraduate and beginning graduate students, this class-tested textbook provides students and other readers with key AI methods and algorithms for solving challenging problems involving systems that behave intelligently in specialized domains such as medical and software diagnostics, financial decision making, speech and text recognition, genetic analysis, and more.
Author: Gunnar Carlsson,
Release Date: 2017-02-02
Machine Intelligence for Healthcare is a must read for physician leaders, health insurance executives, clinical researchers, public health officials, data scientists and software engineers seeking to understand this pivotal innovation in the information revolution in healthcare. MI for Healthcare provides a detailed introduction of Machine Intelligence, then takes the reader on a journey through the basics of machine learning, topological data analysis and applications of machine intelligence software for healthcare and life sciences. Over 20 case studies cover topics related to clinical variation analysis, hospital clinical pathways, population health management, genetic analysis, precision medicine, healthcare revenue cycle, and payment integrity. The book includes a detailed introduction of the mathematics of topology and concepts of machine learning algorithms. This provides an understanding for the central role which machine intelligence software is now playing in the emergence of the "learning healthcare system" and success in the new world of value-based healthcare delivery.
This book presents interdisciplinary research on cognition, mind and behavior from an information processing perspective. It includes chapters on Artificial Intelligence, Decision Support Systems, Machine Learning, Data Mining and Support Vector Machines, chiefly with regard to the data obtained and analyzed in Medical Informatics, Bioinformatics and related disciplines. The book reflects the state-of-the-art in Artificial Intelligence and Cognitive Science, and covers theory, algorithms, numerical simulation, error and uncertainty analysis, as well novel applications of new processing techniques in Biomedical Informatics, Computer Science and its applied areas. As such, it offers a valuable resource for students and researchers from the fields of Computer Science and Engineering in Medicine and Biology.
This book offers valuable new insights into the design of culturally-aware systems. In its first part, it is devoted to presenting selected Culturally-Aware Intelligent Systems devised in the field of Artificial Intelligence and its second part consists of two sub-parts that offer a source of inspiration for building modelizations of Culture and of its influence on the human mind and behavior, to be used in new Culturally-Aware Intelligent Systems. Those sub-parts present the results of experiments conducted in two fields that study Culture and its influence on the human mind’s functions: Cultural Neuroscience and Cross-Cultural Psychology. In this era of globalization, people from different countries and cultures have the opportunity to interact directly or indirectly in a wide variety of contexts. Despite differences in their ways of thinking and reasoning, their behaviors, their values, lifestyles, customs and habits, languages, religions – in a word, their cultures – they must be able to collaborate on projects, to understand each other’s views, to communicate in such a way that they don’t offend each other, to anticipate the effects of their actions on others, and so on. As such, it is of primary importance to understand how culture affects people’s mental activities, such as perception, interpretation, reasoning, emotion and behavior, in order to anticipate possible misunderstandings due to differences in handling the same situation, and to try and resolve them. Artificial Intelligence, and more specifically, the field of Intelligent Systems design, aims at building systems that mimic the behavior of human beings in order to complete tasks more efficiently than humans could by themselves. Consequently, in the last decade, experts and scholars in the field of Intelligent Systems have been increasingly tackling the notion of cultural awareness. A Culturally-Aware Intelligent System can be defined as a system where Culture-related or, more generally, socio-cultural information is modeled and used to design the human-machine interface, or to provide support with the task carried out by the system, be it reasoning, simulation or any other task involving cultural knowledge.