ABSTRACT
With the rapid proliferation of the Internet, traditional Information Retrieval (IR) techniques need to address challenges that stem from information overload by filtering web documents and ranking them in an order that can be perceived to be more relevant and credible to the end-user. In the domain of health care, an increasing number of people turn to the Internet for their health and wellness concerns. The results returned by traditional search engines can therefore be overwhelming and, even worse, inaccurate. As a consequence there is a need to design more "intelligent" web services that pre-process and alter information on the user's behalf. Specifically, this paper describes the design of a personalized search engine that utilizes patient data (either stored in user-managed personal health records or in provider-managed electronic medical records) and couples this with a selective crawling of credible medical information to eliminate search results that appear irrelevant to the user (given the user's "health profile") and rank the remaining results in order of relevance based on the health conditions of users performing the searches. Toward this end, a new ranking algorithm that combines a user's search query and the user's health profile is introduced. Finally, comparisons of the search results for users with different health profiles and diverse queries are presented using this architecture.
- J. Bardram. Hospitals of the future - ubiquitous computing support for medical work. In Proc. of the Hospitals Workshop. Ubihealth, 2003.Google Scholar
- J. Bardram. Applications of context-aware computing in hospital work - examples and design principles. In Proc. of SAC, Cyprus, March 2004. Google ScholarDigital Library
- H. Cao et al. Context-aware query suggestion by mining click-through and session data. KDD'08, 2008. Google ScholarDigital Library
- H. Cao et al. Context-aware query classification. SIGIR'09, 2009. Google ScholarDigital Library
- H. Cao et al. Towards context-aware search by learning a very large variable length hidden markov model from search logs. WWW'09, 2009. Google ScholarDigital Library
- C. Clark. Generating query substitutions. Copely News Service, 2005.Google Scholar
- C. Clark. Patients use of web for health information has pluses and minuses. Copely News Service, 2005.Google Scholar
- A. Dey, G. Abowd, and D. Salber. A conceptual framework and toolkit for supporting the rapid prototyping of context-aware applications. Journal on Human Computer Interaction, Special Issue on Context-Aware Computing, 16(2):97--166, 2001. Google ScholarDigital Library
- Z. Dou et al. A large-scale evaluation and analysis of personalized search strategies. WWW'07, 2007. Google ScholarDigital Library
- E. Burns. U.S. search engine rankings. http://searchenginewatch.com, April 2007.Google Scholar
- R. Goldberg, P. Pitts, and C. Patton. Insta-americans: The empowered (and imperiled) health care consumer in the age of internet medicine. Center for Medicine in the Public Interest, January 2008.Google Scholar
- F. Gou et al. Efficient multiple-click models in web search. WSDM'09, 2009. Google ScholarDigital Library
- T. Joachims. Optimizing search engines using clickthrough data. KDD'02, 2002. Google ScholarDigital Library
- R. Jones et al. Generating query substitutions. WWW'06, 2006. Google ScholarDigital Library
- J. Teevan et al. Information re-retrieval: Repeat queries in yahoo's logs. In SIGIR'07, 2007. Google ScholarDigital Library
- T. Liu. Learning to rank for information retrieval. Foundation and Trends on Information Retrieval, Now Publishers, 2009.Google Scholar
- F. Qiu and J. Cho. Automatic identification of user interest for personalized search. WWW'06, 2006. Google ScholarDigital Library
- E. E. K. Schmidt. Alternative cures for depression how safe are web sites. Psychiatry Research, 129:297--301, 2004.Google ScholarCross Ref
- V. Stanford. Beam me up, dr. mccoy. IEEE Pervasive Computing Magazine, 2(3):13--18, 2003. Google ScholarDigital Library
- T. Lau and E. Horvitz. Patterns of search: Analyzing and modeling web query refinement. ICUM'99, 1999. Google ScholarDigital Library
- A. H. van Bunningen et al. Ranking query results using context-aware preferences. In IEEE 23rd International Conference on Data Engineering Workshop, 2007. Google ScholarDigital Library
- B. Weber, D. J. Derrico, S. Yoon, and P. Sherwill-Navarro. Educating patients to evaluate web-based health care information. Journal of clinical nursing, 19:1371--1377, 2009.Google Scholar
- B. Xiang et al. Context-aware ranking in web search. SIGIR'10, 2010. Google ScholarDigital Library
Index Terms
- An architecture for personalized health information retrieval
Recommendations
Reflections on `Health Care in the Information Society - a Prognosis for the Year 2013`
In the year 2000 a talk was given and later published on how health care could look like in the year 2013. The aim was to "identify priorities in medical informatics research and necessary activities by policymakers in order to ensure an efficient, ...
Assessing the Prognoses on Health Care in the Information Society 2013 - Thirteen Years After
Health care and information technology in health care is advancing at tremendous speed. We analysed whether the prognoses by Haux et al. - first presented in 2000 and published in 2002 [ 1 ] - have been fulfilled in 2013 and which might be the reasons ...
Measuring the awareness of health care providers at Benghazi Medical Center for Health Informatics
ICEMIS'20: Proceedings of the 6th International Conference on Engineering & MIS 2020Health informatics has led to many changes in the health service delivery system. Awareness of health informatics is important and crucial to modern physicians. In general today there is a lack of awareness and understanding of health informatics among ...
Comments