Text Mining of Web-Based Medical Content / / 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 aud...
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Superior document: | Title is part of eBook package: De Gruyter DGBA Backlist Complete English Language 2000-2014 PART1 |
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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 ,
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Physical Description: | 1 online resource (263 p.) |
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Text Mining of Web-Based Medical Content / ed. by Amy Neustein. Berlin ; Boston : De Gruyter, [2014] ©2014 1 online resource (263 p.) text txt rdacontent computer c rdamedia online resource cr rdacarrier text file PDF rda Speech Technology and Text Mining in Medicine and Health Care , 2329-5198 ; 1 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 restricted access http://purl.org/coar/access_right/c_16ec online access with authorization star • 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 Issued also in print. Mode of access: Internet via World Wide Web. In English. Description based on online resource; title from PDF title page (publisher's Web site, viewed 28. Feb 2023) data mining. elektrotechnik. maschinelles lernen. sprachverarbeitung. COMPUTERS / Social Aspects / General. bisacsh Electronic Health Records. Health Mapping Tools. Health-Related Videos. Relationship Extraction Techniques. Speech Processing. Speech-Enabled Web Content. Summarization Techniques. Angel, Bravo-Salgado, contributor. ctb https://id.loc.gov/vocabulary/relators/ctb Armin, R. Mikler, contributor. ctb https://id.loc.gov/vocabulary/relators/ctb Bellika, Johan, contributor. ctb https://id.loc.gov/vocabulary/relators/ctb Bravo-Salgado, Angel, contributor. ctb https://id.loc.gov/vocabulary/relators/ctb Brezovan, Marius , contributor. ctb https://id.loc.gov/vocabulary/relators/ctb Brezovan, Marius, contributor. ctb https://id.loc.gov/vocabulary/relators/ctb Burdescu, Dumitru Dan, contributor. ctb https://id.loc.gov/vocabulary/relators/ctb Chartree, Jedsada, contributor. ctb https://id.loc.gov/vocabulary/relators/ctb Cécile, Paris, contributor. ctb https://id.loc.gov/vocabulary/relators/ctb Denny, Joshua C., contributor. ctb https://id.loc.gov/vocabulary/relators/ctb Dumitru, Dan Burdescu, contributor. ctb https://id.loc.gov/vocabulary/relators/ctb Enrique, Jose, contributor. ctb https://id.loc.gov/vocabulary/relators/ctb Ferreira, Liliana, contributor. ctb https://id.loc.gov/vocabulary/relators/ctb Ghazinour, Kambiz, contributor. ctb https://id.loc.gov/vocabulary/relators/ctb Hanlen, Leif, contributor. ctb https://id.loc.gov/vocabulary/relators/ctb Imambi, S. Sagar , contributor. ctb https://id.loc.gov/vocabulary/relators/ctb Imambi, S. Sagar, contributor. ctb https://id.loc.gov/vocabulary/relators/ctb Jimenez, Tamara, contributor. ctb https://id.loc.gov/vocabulary/relators/ctb Johan, Gustav Bellika, contributor. ctb https://id.loc.gov/vocabulary/relators/ctb Joshua, C. Denny, contributor. ctb https://id.loc.gov/vocabulary/relators/ctb Karlsen, Randi, contributor. ctb https://id.loc.gov/vocabulary/relators/ctb Keith, M. Williams, contributor. ctb https://id.loc.gov/vocabulary/relators/ctb Matwin, Stan, contributor. ctb https://id.loc.gov/vocabulary/relators/ctb Mikler, Armin R., contributor. ctb https://id.loc.gov/vocabulary/relators/ctb Morell, Borrás, contributor. ctb https://id.loc.gov/vocabulary/relators/ctb Morell, Jose Enrique Borras, contributor. ctb https://id.loc.gov/vocabulary/relators/ctb Moses, Oyelami Olufemi, contributor. ctb https://id.loc.gov/vocabulary/relators/ctb Neustein, Amy, contributor. ctb https://id.loc.gov/vocabulary/relators/ctb Neustein, Amy, editor. edt http://id.loc.gov/vocabulary/relators/edt Oyelami, Olufemi, contributor. ctb https://id.loc.gov/vocabulary/relators/ctb Paris, Cecile, contributor. ctb https://id.loc.gov/vocabulary/relators/ctb Rodrigues, Mario, contributor. ctb https://id.loc.gov/vocabulary/relators/ctb Rodrigues, Mário, contributor. ctb https://id.loc.gov/vocabulary/relators/ctb Salcedo, Vicente, contributor. ctb https://id.loc.gov/vocabulary/relators/ctb Sokolova, Marina, contributor. ctb https://id.loc.gov/vocabulary/relators/ctb Stanescu, Liana, contributor. ctb https://id.loc.gov/vocabulary/relators/ctb Suominen, Hanna, contributor. ctb https://id.loc.gov/vocabulary/relators/ctb Teixeira, António Joaquim S., contributor. ctb https://id.loc.gov/vocabulary/relators/ctb Teixeira, António, contributor. ctb https://id.loc.gov/vocabulary/relators/ctb Vicente, Traver Salcedo, contributor. ctb https://id.loc.gov/vocabulary/relators/ctb Williams, Keith M., contributor. ctb https://id.loc.gov/vocabulary/relators/ctb Xu, Hua, contributor. ctb https://id.loc.gov/vocabulary/relators/ctb Title is part of eBook package: De Gruyter DGBA Backlist Complete English Language 2000-2014 PART1 9783110238570 Title is part of eBook package: De Gruyter DGBA Backlist Physical Sciences 2000-2014 (EN) 9783110238518 Title is part of eBook package: De Gruyter DGBA Physical Sciences 2000 - 2014 9783110637212 ZDB-23-GPS Title is part of eBook package: De Gruyter EBOOK PACKAGE Complete Package 2014 9783110369526 ZDB-23-DGG Title is part of eBook package: De Gruyter EBOOK PACKAGE Engineering 2014 9783110369656 ZDB-23-DIW EPUB 9781614519768 print 9781614515418 https://doi.org/10.1515/9781614513902 https://www.degruyter.com/isbn/9781614513902 Cover https://www.degruyter.com/document/cover/isbn/9781614513902/original |
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Angel, Bravo-Salgado, Angel, Bravo-Salgado, Armin, R. Mikler, Armin, R. Mikler, Bellika, Johan, Bellika, Johan, Bravo-Salgado, Angel, Bravo-Salgado, Angel, Brezovan, Marius , Brezovan, Marius , Brezovan, Marius, Brezovan, Marius, Burdescu, Dumitru Dan, Burdescu, Dumitru Dan, Chartree, Jedsada, Chartree, Jedsada, Cécile, Paris, Cécile, Paris, Denny, Joshua C., Denny, Joshua C., Dumitru, Dan Burdescu, Dumitru, Dan Burdescu, Enrique, Jose, Enrique, Jose, Ferreira, Liliana, Ferreira, Liliana, Ghazinour, Kambiz, Ghazinour, Kambiz, Hanlen, Leif, Hanlen, Leif, Imambi, S. Sagar , Imambi, S. Sagar , Imambi, S. Sagar, Imambi, S. Sagar, Jimenez, Tamara, Jimenez, Tamara, Johan, Gustav Bellika, Johan, Gustav Bellika, Joshua, C. Denny, Joshua, C. Denny, Karlsen, Randi, Karlsen, Randi, Keith, M. Williams, Keith, M. Williams, Matwin, Stan, Matwin, Stan, Mikler, Armin R., Mikler, Armin R., Morell, Borrás, Morell, Borrás, Morell, Jose Enrique Borras, Morell, Jose Enrique Borras, Moses, Oyelami Olufemi, Moses, Oyelami Olufemi, Neustein, Amy, Neustein, Amy, Neustein, Amy, Neustein, Amy, Oyelami, Olufemi, Oyelami, Olufemi, Paris, Cecile, Paris, Cecile, Rodrigues, Mario, Rodrigues, Mario, Rodrigues, Mário, Rodrigues, Mário, Salcedo, Vicente, Salcedo, Vicente, Sokolova, Marina, Sokolova, Marina, Stanescu, Liana, Stanescu, Liana, Suominen, Hanna, Suominen, Hanna, Teixeira, António Joaquim S., Teixeira, António Joaquim S., Teixeira, António, Teixeira, António, Vicente, Traver Salcedo, Vicente, Traver Salcedo, Williams, Keith M., Williams, Keith M., Xu, Hua, Xu, Hua, |
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Angel, Bravo-Salgado, Angel, Bravo-Salgado, Armin, R. Mikler, Armin, R. Mikler, Bellika, Johan, Bellika, Johan, Bravo-Salgado, Angel, Bravo-Salgado, Angel, Brezovan, Marius , Brezovan, Marius , Brezovan, Marius, Brezovan, Marius, Burdescu, Dumitru Dan, Burdescu, Dumitru Dan, Chartree, Jedsada, Chartree, Jedsada, Cécile, Paris, Cécile, Paris, Denny, Joshua C., Denny, Joshua C., Dumitru, Dan Burdescu, Dumitru, Dan Burdescu, Enrique, Jose, Enrique, Jose, Ferreira, Liliana, Ferreira, Liliana, Ghazinour, Kambiz, Ghazinour, Kambiz, Hanlen, Leif, Hanlen, Leif, Imambi, S. Sagar , Imambi, S. Sagar , Imambi, S. Sagar, Imambi, S. Sagar, Jimenez, Tamara, Jimenez, Tamara, Johan, Gustav Bellika, Johan, Gustav Bellika, Joshua, C. Denny, Joshua, C. Denny, Karlsen, Randi, Karlsen, Randi, Keith, M. Williams, Keith, M. Williams, Matwin, Stan, Matwin, Stan, Mikler, Armin R., Mikler, Armin R., Morell, Borrás, Morell, Borrás, Morell, Jose Enrique Borras, Morell, Jose Enrique Borras, Moses, Oyelami Olufemi, Moses, Oyelami Olufemi, Neustein, Amy, Neustein, Amy, Neustein, Amy, Neustein, Amy, Oyelami, Olufemi, Oyelami, Olufemi, Paris, Cecile, Paris, Cecile, Rodrigues, Mario, Rodrigues, Mario, Rodrigues, Mário, Rodrigues, Mário, Salcedo, Vicente, Salcedo, Vicente, Sokolova, Marina, Sokolova, Marina, Stanescu, Liana, Stanescu, Liana, Suominen, Hanna, Suominen, Hanna, Teixeira, António Joaquim S., Teixeira, António Joaquim S., Teixeira, António, Teixeira, António, Vicente, Traver Salcedo, Vicente, Traver Salcedo, Williams, Keith M., Williams, Keith M., Xu, Hua, Xu, Hua, |
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author_sort |
Angel, Bravo-Salgado, |
title |
Text Mining of Web-Based Medical Content / |
spellingShingle |
Text Mining of Web-Based Medical Content / Speech Technology and Text Mining in Medicine and Health Care , 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 |
title_full |
Text Mining of Web-Based Medical Content / ed. by Amy Neustein. |
title_fullStr |
Text Mining of Web-Based Medical Content / ed. by Amy Neustein. |
title_full_unstemmed |
Text Mining of Web-Based Medical Content / ed. by Amy Neustein. |
title_auth |
Text Mining of Web-Based Medical Content / |
title_alt |
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 |
title_new |
Text Mining of Web-Based Medical Content / |
title_sort |
text mining of web-based medical content / |
series |
Speech Technology and Text Mining in Medicine and Health Care , |
series2 |
Speech Technology and Text Mining in Medicine and Health Care , |
publisher |
De Gruyter, |
publishDate |
2014 |
physical |
1 online resource (263 p.) Issued also in print. |
contents |
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 |
isbn |
9781614513902 9783110238570 9783110238518 9783110637212 9783110369526 9783110369656 9781614519768 9781614515418 |
issn |
2329-5198 ; |
url |
https://doi.org/10.1515/9781614513902 https://www.degruyter.com/isbn/9781614513902 https://www.degruyter.com/document/cover/isbn/9781614513902/original |
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Not Illustrated |
doi_str_mv |
10.1515/9781614513902 |
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922639748 |
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<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>13636nam a22014055i 4500</leader><controlfield tag="001">9781614513902</controlfield><controlfield tag="003">DE-B1597</controlfield><controlfield tag="005">20230228123812.0</controlfield><controlfield tag="006">m|||||o||d||||||||</controlfield><controlfield tag="007">cr || ||||||||</controlfield><controlfield tag="008">230228t20142014gw fo d z eng d</controlfield><datafield tag="019" ind1=" " ind2=" "><subfield code="a">(OCoLC)960201781</subfield></datafield><datafield tag="020" ind1=" " ind2=" "><subfield code="a">9781614513902</subfield></datafield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1515/9781614513902</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-B1597)212017</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(OCoLC)922639748</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-B1597</subfield><subfield code="b">eng</subfield><subfield code="c">DE-B1597</subfield><subfield code="e">rda</subfield></datafield><datafield tag="041" ind1="0" ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="044" ind1=" " ind2=" "><subfield code="a">gw</subfield><subfield code="c">DE</subfield></datafield><datafield tag="072" ind1=" " ind2="7"><subfield code="a">COM079000</subfield><subfield code="2">bisacsh</subfield></datafield><datafield tag="245" ind1="0" ind2="0"><subfield code="a">Text Mining of Web-Based Medical Content /</subfield><subfield code="c">ed. by Amy Neustein.</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="a">Berlin ;</subfield><subfield code="a">Boston : </subfield><subfield code="b">De Gruyter, </subfield><subfield code="c">[2014]</subfield></datafield><datafield tag="264" ind1=" " ind2="4"><subfield code="c">©2014</subfield></datafield><datafield tag="300" ind1=" " ind2=" "><subfield code="a">1 online resource (263 p.)</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">computer</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">online resource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="347" ind1=" " ind2=" "><subfield code="a">text file</subfield><subfield code="b">PDF</subfield><subfield code="2">rda</subfield></datafield><datafield tag="490" ind1="0" ind2=" "><subfield code="a">Speech Technology and Text Mining in Medicine and Health Care ,</subfield><subfield code="x">2329-5198 ;</subfield><subfield code="v">1</subfield></datafield><datafield tag="505" ind1="0" ind2="0"><subfield code="t">Frontmatter -- </subfield><subfield code="t">Preface -- </subfield><subfield code="t">Contents -- </subfield><subfield code="t">List of authors -- </subfield><subfield code="t">Part I. Methods and techniques for mining biomedical literature and electronic health records -- </subfield><subfield code="t">1. Application of text mining to biomedical knowledge extraction: analyzing clinical narratives and medical literature -- </subfield><subfield code="t">2. Unlocking information in electronic health records using natural language processing: a case study in medication information extraction -- </subfield><subfield code="t">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 -- </subfield><subfield code="t">Part II. Machine Learning Techniques for Mining Medical Search Queries and Health-Related Social Media Posts and Tweets -- </subfield><subfield code="t">4. Predicting dengue incidence in Thailand from online search queries that include weather and climatic variables -- </subfield><subfield code="t">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 -- </subfield><subfield code="t">6. Twitter for health – building a social media search engine to better understand and curate laypersons’ personal experiences -- </subfield><subfield code="t">Part III. Using speech and audio technologies for improving access to online content for the computer-illiterate and the visually impaired -- </subfield><subfield code="t">7. An empirical study of user satisfaction with a health dialogue system designed for the Nigerian low-literate, computer-illiterate, and visually impaired -- </subfield><subfield code="t">8. DVX – the descriptive video exchange project: using crowd-based audio clips to improve online video access for the blind and the visually impaired -- </subfield><subfield code="t">Part IV. Visual data: new methods and approaches to mining radiographic image data and video metadata -- </subfield><subfield code="t">9. Information extraction from medical images: evaluating a novel automatic image annotation system using semantic-based visual information retrieval -- </subfield><subfield code="t">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 -- </subfield><subfield code="t">Editor’s biography</subfield></datafield><datafield tag="506" ind1="0" ind2=" "><subfield code="a">restricted access</subfield><subfield code="u">http://purl.org/coar/access_right/c_16ec</subfield><subfield code="f">online access with authorization</subfield><subfield code="2">star</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="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 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</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="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 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</subfield></datafield><datafield tag="530" ind1=" " ind2=" "><subfield code="a">Issued also in print.</subfield></datafield><datafield tag="538" ind1=" " ind2=" "><subfield code="a">Mode of access: Internet via World Wide Web.</subfield></datafield><datafield tag="546" ind1=" " ind2=" "><subfield code="a">In English.</subfield></datafield><datafield tag="588" ind1="0" ind2=" "><subfield code="a">Description based on online resource; title from PDF title page (publisher's Web site, viewed 28. Feb 2023)</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">data mining.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">elektrotechnik.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">maschinelles lernen.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">sprachverarbeitung.</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">COMPUTERS / Social Aspects / General.</subfield><subfield code="2">bisacsh</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">Electronic Health Records.</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">Health Mapping Tools.</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">Health-Related Videos.</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">Relationship Extraction Techniques.</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">Speech Processing.</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">Speech-Enabled Web Content.</subfield></datafield><datafield tag="653" ind1=" " ind2=" "><subfield code="a">Summarization Techniques.</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Angel, Bravo-Salgado, </subfield><subfield code="e">contributor.</subfield><subfield code="4">ctb</subfield><subfield code="4">https://id.loc.gov/vocabulary/relators/ctb</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Armin, R. 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