Text Mining of Web-Based Medical Content / / ed. by Amy Neustein.

• Superior document: Title is part of eBook package: De Gruyter DGBA Backlist Complete English Language 2000-2014 PART1
• MitwirkendeR: Angel, Bravo-Salgado,; Armin, R. Mikler,; Bellika, Johan,; Bravo-Salgado, Angel,; Brezovan, Marius ,; Brezovan, Marius,; Burdescu, Dumitru Dan,; Chartree, Jedsada,; Cécile, Paris,; Denny, Joshua C.,; Dumitru, Dan Burdescu,; Enrique, Jose,; Ferreira, Liliana,; Ghazinour, Kambiz,; Hanlen, Leif,; Imambi, S. Sagar ,; Imambi, S. Sagar,; Jimenez, Tamara,; Johan, Gustav Bellika,; Joshua, C. Denny,; Karlsen, Randi,; Keith, M. Williams,; Matwin, Stan,; Mikler, Armin R.,; Morell, Borrás,; Morell, Jose Enrique Borras,; Moses, Oyelami Olufemi,; Neustein, Amy,; Oyelami, Olufemi,; Paris, Cecile,; Rodrigues, Mario,; Rodrigues, Mário,; Salcedo, Vicente,; Sokolova, Marina,; Stanescu, Liana,; Suominen, Hanna,; Teixeira, António Joaquim S.,; Teixeira, António,; Vicente, Traver Salcedo,; Williams, Keith M.,; Xu, Hua,
• HerausgeberIn: Neustein, Amy,
• Place / Publishing House: Berlin ;, Boston : : De Gruyter, , [2014]; ©2014
• Year of Publication: 2014
• Language: English
• Series: Speech Technology and Text Mining in Medicine and Health Care , 1
• Physical Description: 1 online resource (263 p.)
• Other title: Frontmatter --; Preface --; Contents --; List of authors --; Part I. Methods and techniques for mining biomedical literature and electronic health records --; 1. Application of text mining to biomedical knowledge extraction: analyzing clinical narratives and medical literature --; 2. Unlocking information in electronic health records using natural language processing: a case study in medication information extraction --; 3. Online health information semantic search and exploration: reporting on two prototypes for performing information extraction on both a hospital intranet and the world wide web --; Part II. Machine Learning Techniques for Mining Medical Search Queries and Health-Related Social Media Posts and Tweets --; 4. Predicting dengue incidence in Thailand from online search queries that include weather and climatic variables --; 5. A study of personal health information posted online: using machine learning to validate the importance of the terms detected by MedDRA and SNOMED in revealing health information in social media --; 6. Twitter for health – building a social media search engine to better understand and curate laypersons’ personal experiences --; Part III. Using speech and audio technologies for improving access to online content for the computer-illiterate and the visually impaired --; 7. An empirical study of user satisfaction with a health dialogue system designed for the Nigerian low-literate, computer-illiterate, and visually impaired --; 8. DVX – the descriptive video exchange project: using crowd-based audio clips to improve online video access for the blind and the visually impaired --; Part IV. Visual data: new methods and approaches to mining radiographic image data and video metadata --; 9. Information extraction from medical images: evaluating a novel automatic image annotation system using semantic-based visual information retrieval --; 10. Helping patients in performing online video search: evaluating the importance of medical terminology extracted from MeSH and ICD-10 in health video title and description --; Editor’s biography
• Format: Mode of access: Internet via World Wide Web.
• ISBN: 9781614513902; 9783110238570; 9783110238518; 9783110637212; 9783110369526; 9783110369656
• ISSN: 2329-5198
• DOI: 10.1515/9781614513902
• Access: restricted access
• Hierarchical level: Monograph
• Statement of Responsibility: ed. by Amy Neustein.

• Includes Text Mining and Natural Language Processing Methods for extracting information from electronic health records and biomedical literature.• Analyzes text analytic tools for new media such as online forums, social media posts, tweets and video sharing.• Demonstrates how to use speech and audio technologies for improving access to online content for the visually impaired. Text Mining of Web-Based Medical Content examines various approaches to deriving high quality information from online biomedical literature, electronic health records, query search terms, social media posts and tweets. Using some of the latest empirical methods of knowledge extraction, the authors show how online content, generated by both professionals and laypersons, can be mined for valuable information about disease processes, adverse drug reactions not captured during clinical trials, and tropical fever outbreaks. Additionally, the authors show how to perform infromation extraction on a hospital intranet, how to build a social media search engine to glean information about patients' own experiences interacting with healthcare professionals, and how to improve access to online health information. This volume provides a wealth of timely material for health informatic professionals and machine learning, data mining, and natural language researchers. Topics in this book include:• Clinical Documents in Electronic Health Records• Summarization Techniques for Online Health Data• Natural Language Processing for Text Mining• Query Expansion Techniques for Tweets• Online Video Data Retrieval of Health-Related Videos• Dengue Fever Outbreaks• Bioemergencies and Social Media Posts• Speech-based Disease Screening for Malaria, Yellow Fever, Typhoid, and Lassa Fever• Audio Access to Online Video Data for the Visually Impaired • Includes Text Mining and Natural Language Processing Methods for extracting information from electronic health records and biomedical literature.• Analyzes text analytic tools for new media such as online forums, social media posts, tweets and video sharing.• Demonstrates how to use speech and audio technologies for improving access to online content for the visually impaired. Text Mining of Web-Based Medical Content examines various approaches to deriving high quality information from online biomedical literature, electronic health records, query search terms, social media posts and tweets. Using some of the latest empirical methods of knowledge extraction, the authors show how online content, generated by both professionals and laypersons, can be mined for valuable information about disease processes, adverse drug reactions not captured during clinical trials, and tropical fever outbreaks. Additionally, the authors show how to perform infromation extraction on a hospital intranet, how to build a social media search engine to glean information about patients' own experiences interacting with healthcare professionals, and how to improve access to online health information. This volume provides a wealth of timely material for health informatic professionals and machine learning, data mining, and natural language researchers. Topics in this book include:• Mining Biomedical Literature and Clinical Narratives • Medication Information Extraction • Machine Learning Techniques for Mining Medical Search Queries • Detecting the Level of Personal Health Information Revealed in Social Media • Curating Layperson’s Personal Experiences with Health Care from Social Media and Twitter • Health Dialogue Systems for Improving Access to Online Content • Crowd-based Audio Clips to Improve Online Video Access for the Visually Impaired • Semantic-based Visual Information Retrieval for Mining Radiographic Image Data • Evaluating the Importance of Medical Terminology in YouTube Video Titles and Descriptions