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Over 1000 stems, roots, and other combining forms, arranged alphabetically. Each entry includes a statement about its Greek or Latin origin, definition, and an example of a typical compound word, given in disarticulated form.
First multi-year cumulation covers six years: 1965-70.
Over 1000 entries to medical suffixes of everyday words, e.g., blood, beneath, and gland. Alphabetical arrangement by words. Entry gives word, various suffixes, and sample terms.
Medical Image Analysis presents practical knowledge on medical image computing and analysis as written by top educators and experts. This text is a modern, practical, self-contained reference that conveys a mix of fundamental methodological concepts within different medical domains. Sections cover core representations and properties of digital images and image enhancement techniques, advanced image computing methods (including segmentation, registration, motion and shape analysis), machine learning, how medical image computing (MIC) is used in clinical and medical research, and how to identify alternative strategies and employ software tools to solve typical problems in MIC. - An authoritative presentation of key concepts and methods from experts in the field - Sections clearly explaining key methodological principles within relevant medical applications - Self-contained chapters enable the text to be used on courses with differing structures - A representative selection of modern topics and techniques in medical image computing - Focus on medical image computing as an enabling technology to tackle unmet clinical needs - Presentation of traditional and machine learning approaches to medical image computing
The abundance of text available in social media and health-related forums and blogs have recently attracted the interest of the public health community to use these sources for opinion mining. This book presents a lexicon-based approach to sentiment analysis in the bio-medical domain, i.e., WordNet for Medical Events (WME). This book gives an insight in handling unstructured textual data and converting it to structured machine-processable data in the bio-medical domain. The readers will discover the following key novelties: 1) development of a bio-medical lexicon: WME expansion and WME enrichment with additional features.; 2) ensemble of machine learning and computational creativity; 3) development of microtext analysis techniques to overcome the inconsistency in social communication. It will be of interest to researchers in the fields of socially-intelligent human-machine interaction and biomedical text mining
A fundamental challenge for medical informatics is to develop and apply better ways of understanding how information technologies and methods can help support the best care for every patient every day given available medical knowledge and resources. In order to provide the most effective healthcare possible, the activities of teams of health professionals have to be coordinated through well-designed processes centered on the needs of patients. For information systems to be accepted and used in such an environment, they must balance standardization based on shared medical knowledge with the flexibility required for customization to the individual patient. Developing innovative approaches to design and build evidence-based careflow management systems is essential for providing the knowledge management infrastructure of health care organizations that seeks to increase performance in delivering high quality care services by efficiently exploiting available resources. Parallel challenges arise in the organization of research at the biological and clinical levels, where the focus on systematically organizing and supporting processes of scientific inquiry by novel informatics methods and databases are in their very early stages. These Proceedings of Medinfo 2004 demonstrate the base of knowledge medical informatics professionals will collectively draw upon in the years ahead to meet these challenges and realize opportunities.