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This book covers all of the major library classification schemes in use in Europe, UK and US; it includes practical exercises to demonstrate their application. Importantly, classifying electronic resources is also discussed. The aim of the book is to demystify a very complex subject, and to provide a sound theoretical underpinning, together with practical advice and development of practical skills. The book fills the gap between more complex theoretical texts and those books with a purely practical approach. Chapters concentrate purely on classification rather than cataloguing and indexing, ensuring a more in-depth coverage of the topic. - Covers the latest Dewey Decimal Classification, 22nd edition - Provides practical advice on which schemes will be most suitable for different types of library collection - Covers classification of electronic resources and taxonomy construction
DHHS publication NIH 00-1535. Revised 5th edition. Incorporates all additions and changes to the classification schedules since the fifth edition was published in 1995. These changes have previously appeared in the "NLM Technical Bulletin." This revision also includes several hundred new entry terms published from 1994 through 1999 in the "NLM Medical Subject Headings Annotated Alphabetic List (MeSH)." Contains copyright material.

NIH Almanac

National Institutes of Health (U.S.). Division of Public Information

Published: 1978

Total Pages: 162


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Applying the latest research findings and practical classroom practices, this book provides thorough coverage of the strategies and skills needed for effective teaching.
Note about this ebook: This ebook exploits many advanced capabilities with images, hypertext, and interactivity and is optimized for EPUB3-compliant book readers, especially Apple's iBooks and browser plugins. These features may not work on all ebook readers. We organize things. We organize information, information about things, and information about information. Organizing is a fundamental issue in many professional fields, but these fields have only limited agreement in how they approach problems of organizing and in what they seek as their solutions. The Discipline of Organizing synthesizes insights from library science, information science, computer science, cognitive science, systems analysis, business, and other disciplines to create an Organizing System for understanding organizing. This framework is robust and forward-looking, enabling effective sharing of insights and design patterns between disciplines that weren’t possible before. The Professional Edition includes new and revised content about the active resources of the "Internet of Things," and how the field of Information Architecture can be viewed as a subset of the discipline of organizing. You’ll find: 600 tagged endnotes that connect to one or more of the contributing disciplines Nearly 60 new pictures and illustrations Links to cross-references and external citations Interactive study guides to test on key points The Professional Edition is ideal for practitioners and as a primary or supplemental text for graduate courses on information organization, content and knowledge management, and digital collections. FOR INSTRUCTORS: Supplemental materials (lecture notes, assignments, exams, etc.) are available at http://disciplineoforganizing.org. FOR STUDENTS: Make sure this is the edition you want to buy. There's a newer one and maybe your instructor has adopted that one instead.
This open access book describes the results of natural language processing and machine learning methods applied to clinical text from electronic patient records. It is divided into twelve chapters. Chapters 1-4 discuss the history and background of the original paper-based patient records, their purpose, and how they are written and structured. These initial chapters do not require any technical or medical background knowledge. The remaining eight chapters are more technical in nature and describe various medical classifications and terminologies such as ICD diagnosis codes, SNOMED CT, MeSH, UMLS, and ATC. Chapters 5-10 cover basic tools for natural language processing and information retrieval, and how to apply them to clinical text. The difference between rule-based and machine learning-based methods, as well as between supervised and unsupervised machine learning methods, are also explained. Next, ethical concerns regarding the use of sensitive patient records for research purposes are discussed, including methods for de-identifying electronic patient records and safely storing patient records. The book’s closing chapters present a number of applications in clinical text mining and summarise the lessons learned from the previous chapters. The book provides a comprehensive overview of technical issues arising in clinical text mining, and offers a valuable guide for advanced students in health informatics, computational linguistics, and information retrieval, and for researchers entering these fields.