machine learning in healthcare informatics intelligent systems reference library

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Machine Learning In Healthcare Informatics

Author : Sumeet Dua
ISBN : 9783642400179
Genre : Computers
File Size : 62. 82 MB
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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.

Artificial Intelligence In Behavioral And Mental Health Care

Author : David D. Luxton
ISBN : 9780128007921
Genre : Psychology
File Size : 68. 13 MB
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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

Machine Learning For Healthcare

Author : John Schrom
ISBN : 1491947004
Genre : Computers
File Size : 31. 65 MB
Format : PDF
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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.

Human And Machine Learning

Author : Jianlong Zhou
ISBN : 9783319904030
Genre : Computers
File Size : 23. 7 MB
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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.

Advances In Biomedical Informatics

Author : Dawn E. Holmes
ISBN : 9783319675138
Genre : Computers
File Size : 35. 51 MB
Format : PDF
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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.

Key Advances In Clinical Informatics

Author : Aziz Sheikh
ISBN : 9780128095256
Genre : Medical
File Size : 58. 97 MB
Format : PDF, Kindle
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Key Advances in Clinical Informatics: Transforming Health Care through Health Information Technology provides a state-of-the-art overview of the most current subjects in clinical informatics. Leading international authorities write short, accessible, well-referenced chapters which bring readers up-to-date with key developments and likely future advances in the relevant subject areas. This book encompasses topics such as inpatient and outpatient clinical information systems, clinical decision support systems, health information technology, genomics, mobile health, telehealth and cloud-based computing. Additionally, it discusses privacy, confidentiality and security required for health data. Edited by internationally recognized authorities in the field of clinical informatics, the book is a valuable resource for medical/nursing students, clinical informaticists, clinicians in training, practicing clinicians and allied health professionals with an interest in health informatics. Presents a state-of-the-art overview of the most current subjects in clinical informatics. Provides summary boxes of key points at the beginning of each chapter to impart relevant messages in an easily digestible fashion Includes internationally acclaimed experts contributing to chapters in one accessible text Explains and illustrates through international case studies to show how the evidence presented is applied in a real world setting

Knowledge Science Engineering And Management

Author : Songmao Zhang
ISBN : 9783319251592
Genre : Computers
File Size : 78. 7 MB
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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.

Computational Intelligence Medicine And Biology

Author : Krzysztof Pancerz
ISBN : 9783319168449
Genre : Computers
File Size : 58. 46 MB
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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.

Text Mining Of Web Based Medical Content

Author : Amy Neustein
ISBN : 9781614513902
Genre : Computers
File Size : 37. 27 MB
Format : PDF
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Demystifying Big Data And Machine Learning For Healthcare

Author : Prashant Natarajan
ISBN : 9781315389318
Genre : Medical
File Size : 21. 13 MB
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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.

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