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A Biomedical Information Retrieval System  based on Clustering for Mobile Devices
 

A Biomedical Information Retrieval System based on Clustering for Mobile Devices

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    A Biomedical Information Retrieval System  based on Clustering for Mobile Devices A Biomedical Information Retrieval System based on Clustering for Mobile Devices Presentation Transcript

    • A Biomedical Information Retrieval System
      based on Clustering for Mobile Devices
      Manuel de la Villa
      Manuel Millán
      Alejandro Muñoz
      Manuel J. Maña
      This work has been partially funded by the Spanish Ministry of Science and Innovation and the European Union from the ERDF (TIN2009-14057-C03-03)
      1
    • Mainindex
      Introduction
      Search and InformationRetrieval
      Clustering
      Visualizationon Mobile Devices
      Conclusions and Future Works
      2
    • Mainindex
      Introduction
      Search and InformationRetrieval
      Clustering
      Visualizationon Mobile Devices
      Conclusions and Future Works
      3
    • Introduction
      Motivation
      Medicalstaffmobility
      • “Hospitals staff might be distributed in space or time and their information needs are highly dependent on contextual conditions.“ (Muñoz et al, 2003)
      • “…PDA use by health professionals shows an evolution in the use ranging from 30% in 2000 to 60% in 2006” (Garrity and El Emam, 2006)
      • the available resources accessible from the PDA at the bedside provided response to 86% of clinical questions, most of them (88.9% - 97.7%) during the rounds of visits (Hauser et al. 2007)
      4
    • Introduction
      Motivation
      Evidence-based medicine
      • “Evidence based medicine is the conscientious, explicit, and judicious use of current best evidence in making decisions about the care of individual patients. The practice of evidence based medicine means integrating individual clinical expertise with the best available external clinical evidence from systematic Research. “(Sackett et Al., 1996)
      • Isusefulan IRS tolocatethe best available external clinical evidence?
    • Introduction
      Motivation
      Wi-fi
      Informationoverload
      PDA
      Point –of-care
      Bestevidence
      Mobility
      CLUMMED
      Efficientaccess
      CLUsteringonMobile MEdicalDevices
      • YAIRS? YetAnother IRS? Novelty?
      • Post-retrievalclustering, orientationtobiomedicaldocuments and mobiledevices
    • Introduction
      Post-retrievalclustering?
      Post-retrievalclusteringis a knowntecniquethatimprovetheorganization of thesearchresults and facilitatenavigationbetweenthem.
      PreviousexperiencesonSearchEnginesusingclustering:
      Univesity Carnegie-Mellon -> Vivisimo -> Clusty
      Interesting? No?
      And if I tellyouthatonMayYippy has paid$5.5M forClusty…?
      7
    • Introduction
      Post-retrievalclusteringonBiomedicine?
    • Index
      Introduction
      Search and InformationRetrieval
      Clustering
      Visualizationon Mobile Devices
      Conclusions and Future Works
      9
    • Search and InformationRetrieval
      A tipicalSchema
      InformationRetrievalSystem
      Given a set of documents and an information need, the goal of IR is to obtain the documents relevant to that need, sort by any criteria and show them to the user.
      Documentsrepresentation
      Documents
      Repository
      Indexing
      Query
      Searching
      Evaluation
      Ranking
      Textprocessing
      Similaritycalculation
      Informationneeded
      Queryrepresentation
      Analysis
      RelevantDocuments
    • Search and InformationRetrieval
      Ourimplementation
      Documentsources:Biomed Central (web crawling in progress)
      TextProcessing:lowercasing, stemming, stop-words ,…
      Lucene for indexing…
    • Search and InformationRetrieval
      Ourimplementation (and II)
      … and Lucene for searching
    • Index
      Introduction
      Search and InformationRetrieval
      Clustering
      Visualizationon Mobile Devices
      Conclusions and Future Works
      13
    • Clustering
      Ourimplementation
      Clustering
      The post-processing clustering is to associate, according to their similarity, a set of documents retrieved from a query in different subsets
      14
    • Clustering
      Why Simple-K-Means?
      Clusteringalgorithm:
      Simple-K-Means vs ExpectationMaximization
      Time it takes to perform the grouping in seconds
      K? Itdependsonthenumber of documentsretrieved.
      15
    • Index
      Introduction
      Search and InformationRetrieval
      Clustering
      Visualizationon Mobile Devices
      Conclusions and Future Works
      16
    • Visualizationon Mobile Devices
      Typicalproblems
      Visualizationon Mobile Devices
      • Restrictions:
      • Compatibilityproblems
      • Screensize
      • Multipleswindowslack
      • Limitednavigation
      • Limitedmemorysize
      • Javascript, cookies
      • Accesibility
      • etc.
      • Solutions:
      • World Wide Web Consortium (W3C): Mobile Web Initiative.
      "The Mobile Web Initiative’sgoalistomakebrowsingthe Web frommobiledevices a reality, toimprove Web contentproductionandaccessformobileusers” (Tim Berners-Lee, W3C Director)
      • Mobile Web BestPractice 1.0.
      17
    • Visualizationon Mobile Devices
      Some best practices
      One web
      making, as far as is reasonable, the same information and
      services available to users irrespective of the device they are using
      Trust in web standards
      HTML compatible with different browsers, Use Stylesheet,
      Content in blocks (<DIV>).
      Avoid known risks
      No pop-ups, No frames, No tables
      Controlling limitations
      No scripting, Standards fonts, Use of color
      Optimized navigation
      Minimal navigation at the top of the page
      Avoid lengthy URI’s
      Probe images and colours
      Reduced image size and resolution, good contrast
      Do it small
      Small pages, only one-direction (vertical) scrolling, easy entry forms (reduced keytyping)
      Limited use of network
      No external links (images…), no download
      Think in users
      Simple language, relevant and limited content, error messages
      And many more…!!!
      18
    • Visualizationon Mobile Devices
      Highresolutioninterface
      19
    • Visualizationon Mobile Devices
      Low resolutioninterface
      20
    • Visualizationon Mobile Devices
      CheckingClusterMed
      21
    • Visualizationon Mobile Devices
      Checkingourproposal
      Ourproposalobteins at hisfirstversion a 76%
    • Visualizationon Mobile Devices
      Ourinterface
      Cancerskin
      23
    • Introduction
      Search and InformationRetrieval
      Clustering
      Visualizationon Mobile Devices
      Conclusions and Future Works
      24
    • Conclusionsandfuturework
      Conclusions
      Conclusions
      • ObjectivesFulfilled
      • Itworks!!!
      • FirstMilestone
      • A workingprototype of anInformationretrievalSystemadaptedforMedicalDevicesbasedon Post-retrievalclustering
      25
    • Conclusionsandfuturework
      Futurework
      … and future work:
      • A lot of…
      • FocusonusingmedicalontologieslikeUMLS Metathesarusfor:
      • Improvetheclusteringquality
      • Enhance the labelling of the groups
      • Visual help, graph with concepts/cluster relationship
      26
    • Conclusionsandfuturework
      Futurework (and II)
      … and future work (and II):
      • Monodocument Summarization
      • Freebase Ajax connection for search support
      • Bilingual (Spanish-English documents)
      • Put into production (crawling and indexing…) any sponsor at the hall???
      27
    • A Biomedical Information Retrieval System
      based on Clustering for Mobile Devices
      Manuel de la Villa
      Manuel J. Maña
      {manuel.villa, manuel.mana}@dti.uhu.es
      Manuel Millán
      Alejandro Muñoz
      {manuel.millan, alejandro.munoz}@alu.uhu.es
      This work has been partially funded by the Spanish Ministry of Science and Innovation and the European Union from the ERDF (TIN2009-14057-C03-03)
      28