Health web science: Social media data for healthcare

Health Web Science: Social Media Data for Healthcare

Volumn , Issue , 2015, Pages 1-148

  Denecke, Kerstin ^a  

^a UNIVERSITY OF LEIPZIG   (Germany)

Author keywords

[No Author keywords available]

Indexed keywords

HEALTH; HEALTH CARE; MEDICAL INFORMATION SYSTEMS; PATIENT TREATMENT; SOCIAL NETWORKING (ONLINE);

APPLICATION EXAMPLES; HEALTH-CARE SYSTEM; INFORMATION GATHERING; INFORMATION SOURCES; PERSONAL MEDICAL INFORMATIONS; SHARED INFORMATION; SOCIAL MEDIA DATUM; SOCIAL MEDIA PLATFORMS;

DATA INTEGRATION;

EID: 84955109360     PISSN: None     EISSN: None     Source Type: Book    
DOI: 10.1007/978-3-319-20582-3     Document Type: Book

References (219)

1. Ben Shneiderman. Web science: a provocative invitation to computer science. Commun. ACM, 50(6):25-27, June 2007.
2. Tim Berners-Lee, Wendy Hall, James Hendler, Nigel Shadbolt, and Daniel J. Weitzner. Creating a science of the web. Science, 313:769-71, 2006.
3. Elizabeth H. Brooks, Grant P. Cumming, and Joanne S. Luciano. Health web science:application of web science to the area of health education and health care. In Proceedings of the second international workshop on Web science and information exchange in the medical web, MedEx '11, pages 11-14, New York, NY, USA, 2011. ACM.
4. P. Eckler, G. Worsowicz, and J.W. Rayburn. Social media and health care: an overview. PM R., 2(11):1046-50, 2010.
5. P. Wicks, M. Massagli, J. Frost, and et al. Sharing health data for better outcomes on patientslikeme. J Med Internet Res, 12(2), 2010.
6. T. Lagu, E.J. Kaufman, D.A. Asch, and K. Armstrong. Content of weblogs written by health professionals. J Gen Intern Med., 23(10):164-76, 2008.
7. G. Eysenbach. Medicine 2.0: social networking, collaboration, participation, apomediation, and openness. J Med Internet Res., 10(3), 2008.
8. Ves Dimov. Casesblog - medical and health blog. health news updated daily by internist and allergist at cleveland clinic. http://casesblog.blogspot.de/, last accessed: 14.03.2015.
9. Kevin Pho. Kevinmd. http://www.kevinmd.com/blog/, last accessed: 14.03.2015.
10. Mayo Clinic. Mayo clinic youtube channel. http://www.youtube.com/user/mayoclinic, last accessed: 14.03.2015.
11. Mayo Clinic. Health care social media list. http://network.socialmedia.mayoclinic.org/hcsmlgrid/, last accessed: 14.03.2015.
12. S. Fox. Health topics. pew research center's internet and american life project. http://www. pewinternet.org/~/media//Files/Reports/2011/PIP_Health_Topics.pdf, 2011. last accessed:20.03.2013.
13. K. Denecke and A. Stewart. Learning from medical social media data: current state and future challenges. In In: White B, King I, Tsang P, editors. Social Media Tools and Platforms in Learning Environments, pages 353-372, 2011.
14. A.Y. Lau, K.A. Siek, L. Fernandez-Luque, and et al. The role of social media for patients and consumer health. Yearb Med Inform., 6:131-8, 2011.
15. Stephanie Medlock, Saeid Eslami, Marjan Askari, Danielle Sent, Sophia E. de Rooij, and Ameen Abu-Hanna. The consequences of seniors seeking health information using the internet and other sources. Studies in Health Technology and Informatics, 192:457-60, 2013.
16. E. Velasco, T. Agheneza, K. Denecke, G. Kirchner, and T. Eckmanns. Social media and internet-based data in global systems for public health surveillance: A systematic review. Milbank Quarterly, 92(1):7-33, 2014.
17. L. Aase, D. Goldman, M. Gould, J. Noseworthy, and F. Timimi. Bringing the Social-media Revolution to Health Care. Mayo Clinic Center for Social-media, 2012.
18. J. Stinson, L. Jibb, P. Nathan, and et al. Development and testing of a multidimensional iphone pain assessment application for adolescents with cancer. iPROCEEDINGS Medicine 2.0 Boston, Sept 15-16 2012. http://www.medicine20congres.com, http://www. medicine20congress.com/ocs/index.php/med/med2012/paper/view/910.
19. S. Khairat and C. Garcia. Introducing a wireless mobile technology to improve diabetes care outcomes among specific minority groups. iPROCEEDINGS Medicine 2.0 Boston, Sept 15-16 2012. http://www.medicine20congres.com, http://www.medicine20congress.com/ocs/index.php/med/med2012/paper/view/900.
20. W.H. Ho, P. Weinstein, D. De Sousa, and et al. A mobile clinical collaboration system for inter-professional team based care in an outpatient setting. iPROCEEDINGS Medicine 2.0 Boston, Sept 15-16 2012. http://www.medicine20congres.com, http://www. medicine20congress.com/ocs/index.php/med/med2012/paper/view/1243.
21. L.L. Struik, J.L. Bottorff, Jung, and C. M. Budgen. Facebook me: The use of social networking sites for gender-sensitive tobacco control messaging. iPROCEEDINGS Medicine 2.0 Boston, Sept 15-16 2012. http://www.medicine20congres.com, http://www. medicine20congress.com/ocs/index.php/med/med2012/paper/view/785.
22. Simon McCallum. Gamification and serious games for personalized health. In B. Blobel (ed.) pHealth 2012, pages 85-90. IOS Press, 2012.
23. G. Llinas, D. Rodriguez-Inesta, J.J.Mira, S. Lorenzo, and C. Aibar. A comparison of websites from spanish, american and british hospitals. Methods Inf Med, 47 (2):124-130, 2008.
24. Pew Internet Project. Health fact sheet. http://www.pewinternet.org/fact-sheets/health-factsheet/, last accessed: 14.03.2015.
25. T. Greenhalgh. Patient and public involvement in chronic illness: beyond the expert patient. BMJ, 338, 2009.
26. S.L. Ayers and J.J. Kronenfeld. Chronic illness and health-seeking information on the internet. Health, 11 (3):327-347, 2007.
27. J. Moreland, T.L. French, and G.P. Cumming. To what extent are people using the internet to obtain health information in the uk: How online health seeking behaviour influences offline behaviour among patients. In Proceedings of Medicine 2.0: Social Media and Web 2.0 (Medicine 2.0). Harvard Medical School, Boston, USA, 2012.
28. DailyStrength. Dailystrength depression support group. http://www.dailystrength.org/c/Depression/support-group, last accessed: 14.03.2015.
29. Docsboard. Docsboard: Dicussion board for doctors. http://www.docsboard.com, last accessed: 14.03.2015.
30. HON. Health on the net foundation. http://www.hon.ch, last accessed: 14.03.2015.
31. P.M. Archambault, T.H. Van De Belt, F.J. Grajales Iii, and et al. Wikis and collaborative writing applications in health care: Preliminary results of a scoping review. iPROCEEDINGS Medicine 2.0 Boston, Sept 15-16 2012. http://www.medicine20congres.com, http://www. medicine20congress.com/ocs/index.php/med/med2012/paper/view/994.
32. Patient Opinion. Patient opinion. https://www.patientopinion.org.uk, last accessed:14.03.2015.
33. B.H. Walter. Telling tales: Treatment stories on an eating disorder support websites. iPROCEEDINGS Medicine 2.0 Boston, Sept 15-16 2012. http://www.medicine20congres. com, http://www.medicine20congress.com/ocs/index.php/med/med2012/paper/view/924.
34. AskaPatient. Ask a patient. http://www.askapatient.com/, last accessed: 14.03.2015.
35. C.A. Brownstein, J.S. Brownstein, D.S. Williams, and et al. The power of social networking in medicine. Nature Biotechnology, 27:888-890, 2009.
36. K. Denecke, M. Krieck, L. Otrusina, and et al. How to exploit twitter for public health monitoring? Methods of Information in Medicine, 52(4):326-39, 2013.
37. Diane J. Standiford. A stellarlife. http://dj-astellarlife.blogspot.de, last accessed: 14.03.2015.
38. Randy Pausch. Randy pausch's update page. http://www.cs.cmu.edu/~pausch/news/index. html, last accessed: 14.03.2015.
39. Amy Tenderich. Diabetesmine. http://www.diabetesmine.com/, last accessed: 14.03.2015.
40. Web MD. Webmd website. http://www.webmd.com, last accessed: 14.03.2015.
41. EveryDayHealth. Every day health media. http://www.everydayhealth.com, last accessed:14.03.2015.
42. Web MD. Medicinenet. http://www.medicinenet.com, last accessed: 14.03.2015.
43. DrugRatingz. Drugratingz. http://www.drugratingz.com/, last accessed: 14.03.2015.
44. Slashdot. Slashdot. http://slashdot.org, last accessed: 14.03.2015.
45. Medical Coding Consultants. Ritecode. http://www.ritecode.com/free_opreports/opreport_ free_index.htm, last accessed: 10.2.2013.
46. NHS. Nhs choices. http://nhs.uk, last accessed: 14.03.2015.
47. Mayo Clinic. Mayo clinic. http://www.mayoclinic.com, last accessed: 14.03.2015.
48. DiabetesDaily. Diabetes daily. http://www.diabetesdaily.com/forum/, last accessed:14.03.2015.
49. K. Denecke and W. Nejdl. How valuable is medical social media data? content analysis of the medical web. Journal of Information Science, 179:1870-1880, 2009.
50. Myca Health. Hellohealth. https://hellohealth.com/solutions/patient-portal/, last accessed:14.03.2015.
51. PatientsLikeMe. Patientslikeme. http://www.patientslikeme.com/, last accessed: 14.03.2015.
52. Rate MD. Ratemd. http://www.ratemds.com/, last accessed: 14.03.2015.
53. Z. Harris. A theory of language and information: a mathematical approach. Clarendon Press, Oxford, 1991.
54. Carol Friedman, Pauline Kra, and Andrey Rzhetsky A. Two biomedical sublanguages:a description based on the theories of zellig harris. Journal of Biomedical Informatics, 35:222-235, 2002.
55. D. A. Campbell and S. B. Johnson. Comparing syntactic complexity in medical and nonmedical corpora. Proc AMIA Symp, pages 90-94, 2001.
56. Naomi Sager and Ngo Thanh Nhan. The computability of strings, transformations, and sublanguage. In Bruce E. Nevin and StephenM. Johnson, editors, The legacy of Zellig Harris:Language and information into the 21st century, pages 79-120. John Benjamins, 2002.
57. Ivor Kovic, Ileana Lulic, and Gordana Brumini. Examining the Medical Blogosphere: An Online Survey of Medical Bloggers. Journal of Medical Internet Research, 10(3), 2008.
58. S.M. Meystre, G.K. Savova, K.C. Kipper-Schuler, and J.F: Hurdle. Extracting information from textual documents in the electronic health record: a review of recent research. Yearbook Med Informat, pages 128-44, 2008.
59. Marcus Mitchell. Penn tree pos tagger. http://www.cis.upenn.edu/~treebank/, last accessed:14.03.2015.
60. Theresa Wilson, Janyce Wiebe, and Paul Hoffmann. Recognizing contextual polarity in phrase-level sentiment analysis. In Proceedings of the Conference on Human Language Technology and Empirical Methods in Natural Language Processing, HLT '05, pages 347-354, Stroudsburg, PA, USA, 2005. Association for Computational Linguistics.
61. Anthony Fader, Stephen Soderland, and Oren Etzioni. Identifying relations for open information extraction. In Proceedings of the Conference on Empirical Methods in Natural Language Processing, EMNLP '11, pages 1535-1545, Stroudsburg, PA, USA, 2011. Association for Computational Linguistics.
62. X. Zhou, X. Zhang, and X. Hu. Dragon toolkit: Incorporating auto-learned semantic knowledge into large-scale text retrieval and mining. In Proceedings of the 19th IEEE International Conference on Tools with Artificial Intelligence (ICTAI), October 29-31, 2007, Patras, Greece, 2007.
63. Kerstin Denecke and Yihan Deng. Sentiment analysis in medical settings - new opportunities and challenges. Artificial Intelligence in Medicine, 2015.
64. OpenCalais. Opencalais website. http://www.opencalais.com, last accessed: 14.03.2015.
65. S. Bird, E Klein, and E. Loper. Natural Language Processing with Python. O'Reilly Media Inc., http://www.nltk.org/book, 2009.
66. Montylingua. Montylingua: A free, commonsense-enriched natural language understander for english. http://web.media.mit.edu/~hugo/montylingua/, last accessed: 14.03.2015.
67. LingPipe. Alias-i: Lingpipe. http://alias-i.com/lingpipe/, last accessed: 14.03.2015.
68. T. Obrebski and M. Stolarski. Uam text tools - a flexible nlp architecture. In Proceedings of LREC 2006, Genova, 2006.
69. C. Grover and et al. A framework for text mining services. In Proceedings of the Third UK e-Science Programme All Hands Meeting (AHM 2004), page 67, 2004.
70. A. Ferrucci and A. Lally. Uima: an architectural approach to unstructured information processing in the corporate research environment. Natural Language Engineering, 10:327-48, 2006.
71. Guergana K. Savova, James J. Masanz, Philip V. Ogren, Jiaping Zheng, Sunghwan Sohn, Karin C. Kipper-Schuler, and Christopher G. Chute. Mayo clinical text analysis and knowledge extraction system (ctakes): architecture, component evaluation and applications. J Am Med Inform Assoc, 17:507-513, 2010.
72. H. Cunningham. Gate, a general architecture for text engineering. Computers and the Humanities, 36:223-254, 2002.
73. Siddhartha Jonnalagadda, Trevor Cohen, Stephen Wu, and Graciela Gonzalez. Enhancing clinical concept extraction with distributional semantics. Journal of Biomedical Informatics, 45(1):129-140, 2012.
74. W.R. Hersh. Information Retrieval: A Health and Biomedical Perspective. Springer-Verlag, 2003.
75. S.M. Weiss, N. Indurkhya, T. Zhang, and F.J. Damerau. Text Mining: Predictive Methods for Analysing Unstructured Information. Springer-Verlag, 2004.
76. Jim Cowie and Wendy Lehnert. Information extraction. Commun. ACM, 39(1):80-91, January 1996.
77. R. Grisham. Chapter 30: Information extraction. In Mitkov R: The Oxford Handbook of Computational Linguistics. Oxford University Press, 2002.
78. Ralph Grishman. Information extraction and speech recognition. In Proceedings of the Broadcast News Transcription and Understanding Workshop, Lansdowne, VA, 1998.
79. Aaron Cohen and William Hersh. A survey of current work in biomedical text mining. Brief Bioinform, 6(1):57-71, January 2005.
80. National Library of Medicine. Unified medical language system. http://www.nlm.nih.gov/research/umls/, last accessed: 14.03.2015.
81. Alexa T. McCray. An upper-level ontology for the biomedical domain. Comp Funct Genom, 4:80-84, 2003.
82. A. Burgun A.T. McCray and O. Bodenreider. Aggregating umls semantic types for reducing conceptual complexity. In Stud Health Technol Inform., pages 216-220, 2001.
83. National Library of Medicine. Medical subject headings. http://www.nlm.nih.gov/pubs/factsheets/mesh.html, last accessed: 14.03.2015.
84. IHTSDO Organisation. International health terminology standards development organisation. http://www.ihtsdo.org/snomed-ct/snomed-ct0/, last accessed: 14.03.2015.
85. Stanford NLP. Stanford named entity recognizer. http://nlp.stanford.edu/software/CRF-NER. shtml, last accessed: 14.03.2015.
86. J. Friedlin and C.J. McDonald. A natural language processing system to extract and code concepts relating to congestive heart failure from chest radiology reports. AMIA Annu Symp Proc., pages 269-73, 2006.
87. D.T. Heinze B.W. Mamlin and C.J. McDonald. Automated extraction and normalization of findings from cancer-related free-text radiology reports. AMIA Annu Symp Proc., pages 420-4, 2003.
88. C. Friedman, P.O. Alderson, J.H. Austin, J.J. Cimino, and S.B. Johnson. A general naturallanguage text processor for clinical radiology. J Am Med Inform Assoc., 1(2):161-174, 1994.
89. Alan R. Aronson. Effective mapping of biomedical text to the umls metathesaurus: The metamap program. In Proceedings of the AMIA 2001, 2001.
90. Carol Friedman, Thomas C. Rindflesch, and Milton Corn. Natural language processing: State of the art and prospects for significant progress, a workshop sponsored by the national library of medicine. Journal of Biomedical Informatics, 46(5):765-773, 2013.
91. G. Savova, S. Bethard, W. Styler, J. Martin, M. Palmer, J. Masanz, and W. Ward. Towards temporal relation discovery from the clinical narrative. In AMIA Annual Symposium Proceedings, volume 2009, page 568. American Medical Informatics Association, American Medical Informatics Association, 2009.
92. Jörn Kottman. Apache opennlp. https://opennlp.apache.org/, last accessed: 14.03.2015.
93. Karin Kipper-Schuler, Vinod Kaggal, James Masanz, Philip Ogren, and Guergana Savova. System evaluation on a named entity corpus from clinical notes. In Language Resources and Evaluation Conference, LREC 2008, pages 3001-7, 2008.
94. Gunther Schadow and Clement J. McDonald. Extracting structured information from free text pathology reports. AMIA Annu Symp Proc., pages 584-588, 2003.
95. W.W. Chapman, M. Fiszman, J.N. Dowling, B.E. Chapman, and T.C. Rindflesch. Identifying respiratory findings in emergency department reports for biosurveillance using metamap. In Stud Health Technol Inform., pages 487-91, 2004.
96. M.E. von Maltzahn S.A. Stewart and S.S. Raza Abidi. Comparing metamap to mgrep as a tool for mapping free text to formal medical lexions. In Proceedings of the 1st International Workshop on Knowledge Extraction and Consolidation from Social-media in conjunction with the 11th International Semantic Web Conference (ISWC 2012), Boston, USA, November 12, 2012, pages 63-77, 2012.
97. C.E.J. Kahn and D.L. Rubin. Automated semantic indexing of figure captions to improve radiology image retrieval. Journal of the American Medical Informatics Association, 16:280-286, 2009.
98. Giuseppe Rizzo and Raphaël Troncy. Nerd: A framework for unifying named entity recognition and disambiguation extraction tools. In Proceedings of the Demonstrations at the 13th Conference of the European Chapter of the Association for Computational Linguistics, EACL '12, pages 73-76, Stroudsburg, PA, USA, 2012. Association for Computational Linguistics.
99. HealthDay. Health day news. http://consumer.healthday.com/, last accessed: 14.03.2015.
100. Alan R. Aronson, Olivier Bodenreider, Dina Demner-Fushman, Kin Wah Fung, Vivian K. Lee, James G. Mork, Aurélie Névéol, Lee Peters, and Willie J. Rogers. From indexing the biomedical literature to coding clinical text: Experience with mti and machine learning approaches. In Proceedings of the Workshop on BioNLP 2007: Biological, Translational, and Clinical Language Processing, BioNLP '07, pages 105-112, Stroudsburg, PA, USA, 2007. Association for Computational Linguistics.
101. Oren Etzioni, Michele Banko, Stephen Soderland, and Daniel S. Weld. Open information extraction from the web. Commun. ACM, 51(12):68-74, December 2008.
102. Oren Etzioni, Anthony Fader, Janara Christensen, Stephen Soderland, and Mausam Mausam. Open information extraction: the second generation. In Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume One, IJCAI'11, pages 3-10. AAAI Press, 2011.
103. Fei Wu and Daniel S. Weld. Open information extraction using wikipedia. In Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics, ACL '10, pages 118-127, Stroudsburg, PA, USA, 2010. Association for Computational Linguistics.
104. Lucia Specia and Enrico Motta. A hybrid approach for extracting semantic relations from texts. In Proceedings of the 2nd Workshop on Ontology Learning and Population, pages 57-64, 2006.
105. Jianhua Li Yun Niu, Xiaodan Zhu and Graeme Hirst. Analysis of polarity information in medical text. In AMIA Annu Symp Proc. 2005, pages 570-574, 2005.
106. Xiaodan Zhu Yun Niu and Graeme Hirst. Using outcome polarity in sentence extraction for medical question-answering. In AMIA Annu Symp Proc. 2006, pages 599-603, 2006.
107. Bing Liu. Sentiment Analysis and Opinion Mining. Synthesis Lectures on Human Language Technologies. Morgan & Claypool Publishers, 2012.
108. Minqing Hu and Bing Liu. Mining and summarizing customer reviews. In Proceedings of the Tenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD '04, pages 168-177, New York, NY, USA, 2004. ACM.
109. G. Mishne. Experiments with mood classification in blog posts. In 1st Workshop on Stylistic Analysis Of Text For Information Access, 2005.
110. Matteo Baldoni, Cristina Baroglio, Viviana Patti, and Paolo Rena. From tags to emotions:Ontology-driven sentiment analysis in the social semantic web. Intelligenza Artificiale, 6(1):41-54, 2012.
111. Bo Pang, Lillian Lee, and Shivakumar Vaithyanathan. Thumbs up?: sentiment classification using machine learning techniques. In Proceedings of the ACL-02 conference on Empirical methods in natural language processing - Volume 10, EMNLP '02, pages 79-86, Stroudsburg, PA, USA, 2002. Association for Computational Linguistics.
112. Bo Pang and Lillian Lee. A sentimental education: Sentiment analysis using subjectivity summarization based on minimum cuts. In Proceedings of the 42Nd Annual Meeting on Association for Computational Linguistics, ACL '04, Stroudsburg, PA, USA, 2004. Association for Computational Linguistics.
113. Peter D. Turney. Thumbs up or thumbs down?: semantic orientation applied to unsupervised classification of reviews. In Proceedings of the 40th Annual Meeting on Association for Computational Linguistics, ACL '02, pages 417-424, Stroudsburg, PA, USA, 2002. Association for Computational Linguistics.
114. Maria Pontiki, Dimitrios Galanis, John Pavlopoulos, Haris Papageorgiou, Ion Androutsopoulos, and Suresh Manandhar. Semeval-2014 task 4: Aspect based sentiment analysis. In Proceedings of the 8th Intern. Workshop on Semantic Evaluation (SemEval 2014), pages 27-35, Dublin, Ireland, August 2014.
115. K. Denecke. Are sentiwordnet scores suited for multi-domain sentiment classification? In Digital Information Management, 2009. ICDIM 2009. Fourth International Conference on, pages 1-6, Nov 2009.
116. A. Montejo-Ráez, E. Martínez-Cámara, M. T. Martín-Valdivia, and L. A. Ureña López. Random walk weighting over sentiwordnet for sentiment polarity detection on twitter. In Proceedings of the 3rd Workshop in Computational Approaches to Subjectivity and Sentiment Analysis, WASSA '12, pages 3-10, Stroudsburg, PA, USA, 2012. Association for Computational Linguistics.
117. Stefano Baccianella, Andrea Esuli, and Fabrizio Sebastiani. Sentiwordnet 3.0: An enhanced lexical resource for sentiment analysis and opinion mining. In Nicoletta Calzolari (Conference Chair), Khalid Choukri, Bente Maegaard, Joseph Mariani, Jan Odijk, Stelios Piperidis, Mike Rosner, and Daniel Tapias, editors, Proceedings of the Seventh International Conference on Language Resources and Evaluation (LREC'10), Valletta, Malta, may 2010. European Language Resources Association (ELRA).
118. Alexandra Balahur, Ralf Steinberger, Mijail Alexandrov Kabadjov, Vanni Zavarella, Erik Van der Goot, Matina Halkia, Bruno Pouliquen, and Jenya Belyaeva. Sentiment analysis in the news. CoRR, abs/1309.6202, 2013.
119. Philip Stone. General inquirer. http://www.wjh.harvard.edu/~inquirer/, last accessed:14.03.2015.
120. Ro Valitutti. Wordnet-affect: an affective extension of wordnet. In In Proceedings of the 4th International Conference on Language Resources and Evaluation, pages 1083-1086, 2004.
121. Saif M. Mohammad and Peter D. Turney. Emotions evoked by common words and phrases:Using mechanical turk to create an emotion lexicon. In Proceedings of the NAACL HLT 2010Workshop on Computational Approaches to Analysis and Generation of Emotion in Text, CAAGET '10, pages 26-34, Stroudsburg, PA, USA, 2010. Association for Computational Linguistics.
122. Lingjia Deng, Yoonjung Choi, and Janyce Wiebe. Benefactive/malefactive event and writer attitude annotation. In ACL (2), pages 120-125, 2013.
123. Lorraine Goeuriot, Jin-Cheon Na, Wai Yan Min Kyaing, Christopher Khoo, Yun-Ke Chang, Yin-Leng Theng, and Jung-Jae Kim. Sentiment lexicons for health-related opinion mining. In Proceedings of the 2Nd ACM SIGHIT International Health Informatics Symposium, IHI '12, pages 219-226, New York, NY, USA, 2012. ACM.
124. B. Ohana, B. Tierney, and S. Delany. Domain independent sentiment classification with many lexicons. In Advanced Information Networking and Applications (WAINA), 2011 IEEE Workshops of International Conference on, pages 632-637, March 2011.
125. Philip J. Stone and Earl B. Hunt. A computer approach to content analysis: Studies using the general inquirer system. In Proceedings of the May 21-23, 1963, Spring Joint Computer Conference, AFIPS '63 (Spring), pages 241-256, New York, NY, USA, 1963. ACM.
126. Grady Ward. Moby lexicon project. http://icon.shef.ac.uk/Moby, last accessed: 14.03.2015.
127. Tanveer Ali, David Schramm, Marina Sokolova, and Diana Inkpen. Can i hear you? sentiment analysis on medical forums. In Proceedings of the Sixth International Joint Conference on Natural Language Processing, pages 667-673, Nagoya, Japan, October 2013. Asian Federation of Natural Language Processing.
128. Lei Xia, Anna Lisa Gentile, James Munro, and José Iria. Improving patient opinion mining through multi-step classification. In Václav Matousek and Pavel Mautner, editors, TSD, volume 5729 of Lecture Notes in Computer Science, pages 70-76. Springer, 2009.
129. Jin-Cheon Na, Wai Yan Min Kyaing, Christopher S. G. Khoo, Schubert Foo, Yun-Ke Chang, and Yin Leng Theng. Sentiment classification of drug reviews using a rule-based linguistic approach. In Hsin-Hsi Chen and Gobinda Chowdhury, editors, ICADL, volume 7634 of Lecture Notes in Computer Science, pages 189-198. Springer, 2012.
130. Marina Sokolova, Stan Matwin, Yasser Jafer, and David Schramm. How joe and jane tweet about their health: Mining for personal health information on twitter. In Galia Angelova, Kalina Bontcheva, and Ruslan Mitkov, editors, RANLP, pages 626-632. RANLP 2011 Organising Committee / ACL, 2013.
131. P. Biyani, C. Caragea, P. Mitra, and et al. Co-training over domain-independent and domaindependent features for sentiment analysis of an online cancer support community. In Advances in Social Networks Analysis and Mining (ASONAM), 2013 IEEE/ACM International Conference on, pages 413-17, 2013.
132. N. Ofek, C. Caragea, L. Rokach, P. Biyani, and et al. Improving sentiment analysis in an online cancer survivor community using dynamic sentiment lexicon. In Social Intelligence and Technology (SOCIETY), 2013 International Conference on, pages 109-13, 2013.
133. Phillip Smith and Mark Lee. Cross-discourse development of supervised sentiment analysis in the clinical domain. In Proceedings of the 3rd Workshop in Computational Approaches to Subjectivity and Sentiment Analysis, WASSA '12, pages 79-83, Stroudsburg, PA, USA, 2012. Association for Computational Linguistics.
134. H. Sharif, A. Abbasi, F. Zafar, and D. Zimbra. Detecting adverse drug reactions using a sentiment classification framework. In Proceedings of the sixth ASE international conference on social computing (SocialCom)s, 2014.
135. Marina Sokolova and Victoria Bobicev. What sentiments can be found in medical forums. In Proceedings of Recent Advances in Natural Language Processing, pages 633-639, Hissar, Bulgaria, September 2013.
136. S. Melzi, A. Abdaoui, J. Aze, S. Bringay, P. Poncelet, and F. Galtier. Patients rationale:Patient knowledge retrieval from health forums. In Proceedings of the eTelemed 2014: Sixth Conference on eHealth, Telemedicine and Social Medicine, pages 140-145, 2014.
137. Yun Niu, Xiaodan Zhu, Jianhua Li, and Graeme Hirst. Analysis of polarity information in medical text. In In: Proceedings of the American Medical Informatics Association 2005 Annual Symposium, pages 570-574, 2005.
138. Abeed Sarker, Diego Molla, and Cecile Paris. Outcome polarity identification of medical papers. In Proceedings of the Australasian Language Technology AssociationWorkshop 2011, pages 105-114, Canberra, Australia, December 2011.
139. John P. Pestian, Pawel Matykiewicz, Michelle Linn-Gust, Brett South, Ozlem Uzuner, Jan Wiebe, Kevin B. Cohen, John Hurdle, and Christopher Brew. Sentiment analysis of suicide notes: A shared task. Biomedical Informatics Insights, 5:3-16, 01-2012.
140. Erik Cambria, Tim Benson, Chris Eckl, and Amir Hussain. Sentic proms: Application of sentic computing to the development of a novel unified framework for measuring health-care quality. Expert Syst. Appl., 39(12):10533-10543, September 2012.
141. H. Liu and P. Singh. Conceptnet — a practical commonsense reasoning tool-kit. BT Technology Journal, 22(4):211-226, October 2004.
142. Erik Cambria, Andrew Livingstone, and Amir Hussain. The hourglass of emotions. In Proceedings of the 2011 International Conference on Cognitive Behavioural Systems, COST'11, pages 144-157, Berlin, Heidelberg, 2012. Springer-Verlag.
143. P. G. Mutalik, A. Deshpande, and P. M. Nadkarni. Use of general-purpose negation detection to augment concept indexing of medical documents: a quantitative study using the UMLS. Journal of the American Medical Informatics Association: JAMIA, 8(6):598-609, 2001.
144. Wendy Webber Chapman, Will Bridewell, Paul Hanbury, Gregory F. Cooper, and Bruce G. Buchanan. A simple algorithm for identifying negated findings and diseases in discharge summaries. Journal of Biomedical Informatics, 34(5):301-310, 2001.
145. Nigel Collier. Uncovering text mining: A survey of current work on web-based epidemic intelligence. Global Public Health, pages 731-749, 2012.
146. C. Paquet, D. Coulombier, R. Kaier, and M. Ciotti. Epidemic intelligence: a new framework for strengthening disease surveillance in europe. Euro Surveillance, 11(12), 2006.
147. J. P. Linge, R. Steinberger, F. Fuart, S. Bucci, J. Belyaeva, M. Gemo, D. Al-Khudhairy, R. Yangarber, and E. van der Goot. Medisys-medical information system. In Advanced ICTs for Disaster Management and Threat Detection: Collaborative and Distributed Frameworks. IGI Global Press, 2010.
148. P. von Etter, S. Huttunen, A. Vihavainen, M. Vuorinen, and R. Yangarber. Assessment of utility in web mining for the domain of public health. In Proceedings of the NAACL HLT 2010 Second Louhi Workshop on Text and Data Mining of Health Documents, pages 29-37, 2012.
149. A.F. Dugas, Y.-H. Hsieh, S.R. Levin, and et al. Google flu trends: Correlation with emergency department influenza rates and crowding metrics. Clin Infect Dis 2012, 54 (4):463-469.
150. S. Cook, C. Conrad, A.L. Fowlkes, and M.H. Mohebbi. Assessing google flu trends performance in the united states during the 2009 influenza virus a (h1n1) pandemic. PLoS One 2011, 6 (8), 2011.
151. H.A. Carneiro and E. Mylonakis. Google trends: a webbased tool for real-time surveillance of disease outbreaks. Clin Infect Dis 2009, 49 (10):1557-64, 2009.
152. G. Backfried, C. Schmidt, M. Pfeiffer, G. Quirchmayr, M. Glanzer, and K. Rainer. Open source intelligence for disaster management. intelligence and security informatics conference (eisic). pages 254-58, 2012.
153. Multilingual cross-domain temporal tagger heideltime, http://dbs.ifi.uni-heidelberg.de/heideltime (last access: 24.04.2013).
154. Stanford named entity recognizer, http://nlp.stanford.edu/software/crf-ner.shtml (last access:24.04.2013).
155. Avare Stewart,Matthew Smith, and Wolfgang Nejdl. A transfer approach to detecting disease reporting events in blog social media. In HT 2011, pages 271-80, 2011.
156. Alessandro Moschitti, Daniele Pighin, and Roberto Basili. Semantic Role Labeling via Tree Kernel Joint Inference. In Proceedings of the Tenth Conference on Computational Natural Language Learning (CoNLL-X), pages 61-68, New York City, June 2006. Association for Computational Linguistics.
157. L.C. Madoff. Promed-mail: An early warning system for emerging disease. Clin Infect Dis 2004, 39 (2):227-32, 2004.
158. Michael P. Coston. Avian flu diary. http://afludiary.blogspot.de/, last accessed: 14.03.2015.
159. Michael Hoehle. Surveillance: An r package for the surveillance of infectious diseases. Computational Statistics, 22 (4):571-82, 2007.
160. G. Rossi, L. Lampugnani, and M. Marchi. An approximate cusum procedure for surveillance of health events. Statistics in Medicine, 18:2111-2122, 1999.
161. P. Farrington, N. Andrews, A. Beale, and M. Catchpole. A statistical algorithm for the early detection of outbreaks of infectious disease. J R Statist Soc A, 159:547-563, 1996.
162. Jamila Hanan. Medworm. http://www.medworm.com/, last accessed: 14.03.2015.
163. K. Denecke. An architecture for diversity-aware search for medical web content. Methods of Information in Medicine, 51(6):549-56, 2012.
164. T. Vanhecke, M. Barnes, J. Zimmerman, and S. Shoichet. PubMed vs. HighWire Press: A head-to-head comparison of two medical literature search engines. Computers in Biology and Medicine, 37(9):1252-1258, September 2007.
165. P. Daumke, K. Marko, M. Propat, S. Schulz, and R. Klar. Biomedical information retrieval across languages. Med Inform Internet Med, 32(2):131-47, 2007.
166. P. Daumke, S. Schulz, M.L. Müller, W. Dzeyk, L. Prinzen, E.J. Pacheco, P.S. Cancian, P. Nohama, and K. Marko. Subword-based semantic retrieval of clinical and bibliograpic documents. Methods Inf Med, 49(2):141-47, 2010.
167. Martin Krallinger and Alfonso Valencia. Text-mining and information-retrieval services for molecular biology. Genome Biology, 6(7):224+, 2005.
168. T.G. Vit Novacek and S. Handschuh. Coraal towards deep exploitation of textual resources in life sciences. Lecture Notes in Computer Science. Berlin/Heidelberg, 5651/2009:206-215, 2009.
169. S. Dumais F. Radlinski. Improving personalized web search using result diversification. In SIGIR 2006: Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval. New York, USA, pages 691-692, 2006.
170. M.A. Hearst and J.O. Pedersen. Reexamining the cluster hypothesis: scatter/gather on retrieval results. In Proceedings of the 19th annual international ACM SIGIR conference on Research and development in information retrieval, SIGIR 1996. New York, USA, pages 76-84, 1996.
171. D. Carmel A. Anagnostopoulos, A.Z. Broder. Sampling search-engine results. In Proceedings of the 14th international conference on World Wide Web: New York, USA, pages 245-256, 2005.
172. Chein-Shung Hwang and Show-Fen Lin. Hill climbing for diversity retrieval. Computer Science and Information Engineering, World Congress on, 5:154-158, 2009.
173. Rakesh Agrawal, Sreenivas Gollapudi, Alan Halverson, and Samuel Ieong. Diversifying search results. In Proceedings of the Second ACM International Conference on Web Search and Data Mining, WSDM '09, pages 5-14, New York, NY, USA, 2009. ACM.
174. Sreenivas Gollapudi and Aneesh Sharma. An axiomatic approach for result diversification. In Proceedings of the 18th International Conference on World Wide Web, WWW '09, pages 381-390, New York, NY, USA, 2009. ACM.
175. Enrico Minack, Gianluca Demartini, andWolfgang Nejdl. Current approaches to search result diversification. In Proc. of 1st Intl. Workshop on Living Web, 2009.
176. Wisam Dakka and Panagiotis G. Ipeirotis. Automatic extraction of useful facet hierarchies from text databases. In Proceedings of the 2008 IEEE 24th International Conference on Data Engineering, ICDE '08, pages 466-475, Washington, DC, USA, 2008. IEEE Computer Society.
177. Marti A. Hearst. Clustering versus faceted categories for information exploration. Commun. ACM, 49(4):59-61, April 2006.
178. J. Diederich and W. T. Balke. Automatically created concept graphs using descriptive keywords in the medical domain. Methods of Information in Medicine, 47(3):241-50, 2008.
179. SJ. Darmoni, JP. Leroy, F. Baudic, M. Douyere, J. Piot, and B. Thrion. Cismef: a structured health resource guide. Methods of Information in Medicine, 39(1):30-35, 2000.
180. Angelos Hliaoutakis, Giannis Varelas, Euripides G. M. Petrakis, and Evangelos Milios. Medsearch: A retrieval system for medical information based on semantic similarity. In In Proceedings of ECDL, pages 512-515, 2006.
181. Gang Luo. Design and evaluation of the imed intelligent medical search engine. In Proceedings of the 2009 IEEE International Conference on Data Engineering, ICDE '09, pages 1379-1390, Washington, DC, USA, 2009. IEEE Computer Society.
182. Alchemy API. Alchemyapi. http://www.alchemyapi.com/, last accessed: 14.03.2015.
183. Andrea Esuli and Fabrizio Sebastiani. Sentiwordnet: A publicly available lexical resource for opinion mining. In In Proceedings of the 5th Conference on Language Resources and Evaluation (LREC 2006), pages 417-422, 2006.
184. Mark Hall, Eibe Frank, Geoffrey Holmes, Bernhard Pfahringer, Peter Reutemann, and Ian H. Witten. The weka data mining software: An update. SIGKDD Explor. Newsl., 11(1):10-18, November 2009.
185. Ian H. Witten and Eibe Frank. Data Mining: Practical Machine Learning Tools and Techniques. The Morgan Kaufmann Series in DataManagement Systems. Morgan Kaufmann Publishers, San Francisco, CA, 2nd edition, 2005.
186. H. Mueller, C. Boyer, A. Gaudinat,W. Hersh, and A. Geissbuhler. Analyzing web log files of the health on the net honmedia search engine to define typical image search tasks for image retrieval evaluation. In Stud Health Technol Inform 129 (Pt 2), pages 1319-1323, 2007.
187. Filip Radlinski and Nick Craswell. Comparing the sensitivity of information retrieval metrics. In Proceedings of the 33rd International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR '10, pages 667-674, New York, NY, USA, 2010. ACM.
188. Kalervo Järvelin and Jaana Kekäläinen. Ir evaluation methods for retrieving highly relevant documents. In Proceedings of the 23rd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR '00, pages 41-48, New York, NY, USA, 2000. ACM.
189. N.H. Shah C. Jonquet and M.A. Musen. The open biomedical annotator. In Summit on Translat Bioinforma, pages 56-60, 2009.
190. Tagesspiegel. Hasso-plattner-institut stoppt facebook-projekt der schufa. http://www. tagesspiegel.de/politik/datenschutz-hasso-plattner-institut-stoppt-facebook-projekt-derschufa/6726234.html, last accessed: 14.03.2015.
191. Peter V. Conveney, Vanessa Diaz-Zuccarini, Peter Hunter, and Marco Viceconti (eds.). Computational Biomedicine. Oxford University Press, 2014.
192. Samuel Thompson. The nature of Philiospophy: An Introduction. Holt, Rinehart, and Winston, 1961.
193. I. Convery and D. Cox. A review of research ethics in internet-based research. Practitioner Research in Higher Education, pages 50-57, 2012.
194. IMIA. Imia social media working group. http://www.imia-medinfo.org/new2/node/289, last accessed: 14.03.2015.
195. Michal Kosinski, David Stillwell, and Thore Graepel. Private traits and attributes are predictable from digital records of human behaviour. Proceedings of the National Academy of Sciences of the United States of America, 2013.
196. Gunther Eysenbach and James E Till. Ethical issues in qualitative research on internet communities. BMJ, 323(7321):1103-1105, 2001.
197. C.S. Bond, O.H. Ahmed, M. Hind, B. Thomas, and J. Hewitt-Taylor. The conceptual and practical ethical dilemmas of using health discussion board posts as research data. J Med Internet Res., 17(6), 2013.
198. Megan A. Moreno, Alison Grant, Lauren Kacvinsky, Peter Moreno, and Michael Fleming. Older adolescents' views regarding participation in facebook research. Journal of Adolescent Health, 51(5):439-444, 2012.
199. Michael J. Payette, Douglas Albreski, and Jane M. Grant-Kels. You'd know if you "friended" me on facebook: Legal, moral, and ethical considerations of online social media. Journal of American Academy of Dermatology, 69:305-7, 2013.
200. F. Gholami-Kordkheili, V. Wild, and D. Strech. The impact of social media on medical professionalism: a systematic qualitative review of challenges and opportunities. J Med Internet Res., 15(8), 2013.
201. K.C. Chretien and T. Kind. Social media and clinical care: ethical, professional, and social implications. Circulation, pages 1413-1421, 2013.
202. J.M. Farnan, Sulmasy L. Snyder, B.K. Worster, H.K. Chaudhry, J.A. Rhyne, V.M. Arora, and et al. Online medical professionalism: Patient and public relationships: Policy statement from the american college of physicians and the federation of state medical boards. Ann Intern Med., pages 620-627, 2013.
203. A. Markham and E. Buchanan. Ethical Decision-Making and Internet Research: Recommendations from the AoIR Ethics Workin Committee. Ethics Working Committee, 2012.
204. Medical Association. Opinion 9.124 - professionalism in the use of social media. URL:http://www.ama-assn.org/resources/doc/code-medical-ethics/9124a.pdf (last accessed:08.12.2013), 2013.
205. U. Huw. Socio-economic research in the information society. URL: http://www. respectproject.org/pubs/ethics.php (last access: 10.12.2013), 2002.
206. H.U. Lemke and L. Berliner. Integration von informationen und cas-systemen mit einem therapy imaging and model management system (timms). In S. Eulenstein P.M. Schlag and T. Lange, editors, Computerassistierte Chirurgie, 1. Edition, pages 311-32. Elsevier, 2011.
207. O. Doessel. Patientenmodelle. pages 14-19, 2012.
208. L.C. van der Gaag, S. Renooij, C.L.M. Witteman, B.M.P. Aleman, and B.G. Taal. Probabilities for a probabilistic network: a case study in oesophageal cancer. Artificial Intelligence in Medicine, pages 123-48, 2002.
209. L. Leibovici, M. Paul, A. D. Nielsen, E. Tacconelli, and S. Andreassen. The treat project:decision support and prediction using causal probabilistic networks. International Journal of Antimicrobial Agents, 30 (Supplement 1):93-102, 2007.
210. K. Denecke. Model-based decision support: Requirements and future for its application in surgery. Biomedical Engineering/Biomedizinische Technik, Biomed Tech, 56 (Suppl 1), 2013.
211. Agnieszka Oniko andMarek J. Druzdzel. Impact of precision of bayesian network parameters on accuracy of medical diagnostic systems. Artif. Intell. Med., 57(3):197-206, March 2013.
212. M. Cypko, M. Stoehr, K. Denecke, A. Dietz, and H.U. Lemke. User interaction with mebns for large patient-specific treatment decision models with an example for laryngeal cancer. Int J CARS, Volume 9 (Suppl 1), 2014.
213. Gregory F. Cooper and Edward Herskovits. A bayesian method for the induction of probabilistic networks from data. In MACHINE LEARNING, pages 309-347, 1992.
214. Peter Antal, Geert Fannes, Dirk Timmerman, Yves Moreau, and Bart De Moor. Using literature and data to learn bayesian networks as clinical models of ovarian tumors. Artificial Intelligence in Medicine, 30(3):257-281, 2004.
215. O.W. Tchami, M-C. L'Homme, and N. Grabar. Discovering semantic frames for a contrastive study of verbs in medical corpora. Terminologie Intelligence Artificielle (TIA), 2013.
216. Y. Wang, S. Pakhomov, N.E. Burkart, J.O. Ryan, and G.B. Melton. A study of actions in operative notes. In AMIA Annu Symp Proc., pages 1431-40, 2012.
217. O. Patterson and J.F. Hurdle. Document clustering of clinical narratives: a systematic study of clinical sublanguages. AMIA Annu Symp Proc., pages 1099-1107, 2011.
218. E. Miscin. Use of corpus analysis tools in medical corpus processing. INFuture2013:"Information Governance", pages 187-196, 2013.
219. Andreas M. Kaplan and Michael Haenlein. Users of the world, unite! the challenges and opportunities of social media. Business Horizons, 53(1):59-68, 2010.