LEADER 08958nam 2200805 450 001 9910814899003321 005 20210423022039.0 010 $a1-61451-390-2 010 $a1-61451-976-5 024 7 $a10.1515/9781614513902 035 $a(CKB)3360000000515050 035 $a(EBL)1249974 035 $a(SSID)ssj0001403095 035 $a(PQKBManifestationID)11852115 035 $a(PQKBTitleCode)TC0001403095 035 $a(PQKBWorkID)11365622 035 $a(PQKB)10968813 035 $a(DE-B1597)212017 035 $a(OCoLC)922639748 035 $a(OCoLC)960201781 035 $a(DE-B1597)9781614513902 035 $a(Au-PeEL)EBL1249974 035 $a(CaPaEBR)ebr11006189 035 $a(CaONFJC)MIL806236 035 $a(OCoLC)900092935 035 $a(CaSebORM)9781614519768 035 $a(MiAaPQ)EBC1249974 035 $a(EXLCZ)993360000000515050 100 $a20140725h20142014 uy| 0 101 0 $aeng 135 $aur|nu---|u||u 181 $ctxt 182 $cc 183 $acr 200 00$aText mining of web-based medical content /$fedited by Amy Neustein 210 1$aBerlin :$cBoston :$cDe Gruyter,$d[2014] 210 4$d©2014 215 $a1 online resource (286 p.) 225 1 $aSpeech technology and text mining in medicine and healthcare ;$vvolume 1 300 $aDescription based upon print version of record. 311 $a1-61451-541-7 320 $aIncludes bibliographical references. 327 $tFront matter --$tPreface --$tContents --$tList of authors --$tPart I. Methods and techniques for mining biomedical literature and electronic health records --$t1. Application of text mining to biomedical knowledge extraction: analyzing clinical narratives and medical literature /$rNeustein, Amy / Imambi, S. Sagar / Rodrigues, Mário / Teixeira, António / Ferreira, Liliana --$t2. Unlocking information in electronic health records using natural language processing: a case study in medication information extraction /$rXu, Hua / Joshua, C. Denny --$t3. 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 /$rTeixeira, António / Ferreira, Liliana / Rodrigues, Mário --$tPart II. Machine Learning Techniques for Mining Medical Search Queries and Health-Related Social Media Posts and Tweets --$t4. Predicting dengue incidence in Thailand from online search queries that include weather and climatic variables /$rChartree, Jedsada / Angel, Bravo-Salgado / Jimenez, Tamara / Armin, R. Mikler --$t5. A study of personal health information posted online: using machine learning to validate the importance of the terms detected by MedDRA and SNOMED in revealing health information in social media /$rGhazinour, Kambiz / Sokolova, Marina / Matwin, Stan --$t6. Twitter for health - building a social media search engine to better understand and curate laypersons' personal experiences /$rSuominen, Hanna / Hanlen, Leif / Cécile, Paris --$tPart III. Using speech and audio technologies for improving access to online content for the computer-illiterate and the visually impaired --$t7. An empirical study of user satisfaction with a health dialogue system designed for the Nigerian low-literate, computer-illiterate, and visually impaired /$rOyelami, Olufemi --$t8. DVX - the descriptive video exchange project: using crowd-based audio clips to improve online video access for the blind and the visually impaired /$rKeith, M. Williams --$tPart IV. Visual data: new methods and approaches to mining radiographic image data and video metadata --$t9. Information extraction from medical images: evaluating a novel automatic image annotation system using semantic-based visual information retrieval /$rDumitru, Dan Burdescu / Stanescu, Liana / Brezovan, Marius --$t10. Helping patients in performing online video search: evaluating the importance of medical terminology extracted from MeSH and ICD-10 in health video title and description /$rKarlsen, Randi / Enrique, Jose / Morell, Borrás / Johan, Gustav Bellika / Vicente, Traver Salcedo --$tEditor's biography 330 $a? 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 information 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 330 $a? 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 information 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 410 0$aSpeech technology and text mining in medicine and healthcare ;$vv. 1. 606 $aData mining 606 $aMedicine$xResearch 606 $aInternet 606 $aMedical informatics 610 $aElectronic Health Records. 610 $aHealth Mapping Tools. 610 $aHealth-Related Videos. 610 $aRelationship Extraction Techniques. 610 $aSpeech Processing. 610 $aSpeech-Enabled Web Content. 610 $aSummarization Techniques. 615 0$aData mining. 615 0$aMedicine$xResearch. 615 0$aInternet. 615 0$aMedical informatics. 676 $a610.285/6 702 $aNeustein$b Amy 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910814899003321 996 $aText mining of web-based medical content$93914934 997 $aUNINA