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On iterative intelligent medical search

Published: 20 July 2008 Publication History

Abstract

Searching for medical information on the Web has become highly popular, but it remains a challenging task because searchers are often uncertain about their exact medical situations and unfamiliar with medical terminology. To address this challenge, we have built an intelligent medical Web search engine called iMed, which uses medical knowledge and an interactive questionnaire to help searchers form queries. This paper focuses on iMed's iterative search advisor, which integrates medical and linguistic knowledge to help searchers improve search results iteratively. Such an iterative process is common for general Web search, and especially crucial for medical Web search, because searchers often miss desired search results due to their limited medical knowledge and the task's inherent difficulty. iMed's iterative search advisor helps the searcher in several ways. First, relevant symptoms and signs are automatically suggested based on the searcher's description of his situation. Second, instead of taking for granted the searcher's answers to the questions, iMed ranks and recommends alternative answers according to their likelihoods of being the correct answers. Third, related MeSH medical phrases are suggested to help the searcher refine his situation description. We demonstrate the effectiveness of iMed's iterative search advisor by evaluating it using real medical case records and USMLE medical exam questions.

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  • (2018)Effects of Language and Terminology of Query Suggestions on the Precision of Health SearchesExperimental IR Meets Multilinguality, Multimodality, and Interaction10.1007/978-3-319-98932-7_9(101-111)Online publication date: 15-Aug-2018
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cover image ACM Conferences
SIGIR '08: Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
July 2008
934 pages
ISBN:9781605581644
DOI:10.1145/1390334
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Publication History

Published: 20 July 2008

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Author Tags

  1. intelligent medical web search engine
  2. iterative search process
  3. language model
  4. medical knowledge
  5. medical query

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Cited By

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  • (2020)De-Health: All Your Online Health Information Are Belong to Us2020 IEEE 36th International Conference on Data Engineering (ICDE)10.1109/ICDE48307.2020.00143(1609-1620)Online publication date: Apr-2020
  • (2020)Focused Query Expansion with Entity Cores for Patient-Centric Health SearchThe Semantic Web – ISWC 202010.1007/978-3-030-62419-4_31(547-564)Online publication date: 1-Nov-2020
  • (2018)Effects of Language and Terminology of Query Suggestions on the Precision of Health SearchesExperimental IR Meets Multilinguality, Multimodality, and Interaction10.1007/978-3-319-98932-7_9(101-111)Online publication date: 15-Aug-2018
  • (2017)Semantic analysis for enhanced medical retrieval2017 IEEE International Conference on Systems, Man, and Cybernetics (SMC)10.1109/SMC.2017.8122762(1121-1126)Online publication date: Oct-2017
  • (2017)Effects of language and terminology of query suggestions on medical accuracy considering different user characteristicsJournal of the Association for Information Science and Technology10.1002/asi.2387468:9(2063-2075)Online publication date: 1-Sep-2017
  • (2016)Towards organizing health knowledge on community-based health servicesEURASIP Journal on Bioinformatics and Systems Biology10.1186/s13637-016-0053-x2016:1Online publication date: 17-Nov-2016
  • (2016)Cyberchondria: Parsing Health Anxiety From Online BehaviorPsychosomatics10.1016/j.psym.2016.02.00257:4(390-400)Online publication date: Jul-2016
  • (2016)Effects of Language and Terminology on the Usage of Health Query SuggestionsExperimental IR Meets Multilinguality, Multimodality, and Interaction10.1007/978-3-319-44564-9_7(83-95)Online publication date: 23-Aug-2016
  • (2015)Disease Inference from Health-Related Questions via Sparse Deep LearningIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2015.239929827:8(2107-2119)Online publication date: 1-Aug-2015
  • (2015)Diversity-aware retrieval of medical recordsComputers in Industry10.1016/j.compind.2014.09.00469:C(81-91)Online publication date: 1-May-2015
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