1. 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

2. Mainindex Introduction Search and InformationRetrieval Clustering Visualizationon Mobile Devices Conclusions and Future Works 2

3. Mainindex Introduction Search and InformationRetrieval Clustering Visualizationon Mobile Devices Conclusions and Future Works 3

5. “…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)

6. 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

10. Introduction Post-retrievalclusteringonBiomedicine?

11. Index Introduction Search and InformationRetrieval Clustering Visualizationon Mobile Devices Conclusions and Future Works 9

12. 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

13. Search and InformationRetrieval Ourimplementation Documentsources:Biomed Central (web crawling in progress) TextProcessing:lowercasing, stemming, stop-words ,… Lucene for indexing…

14. Search and InformationRetrieval Ourimplementation (and II) … and Lucene for searching

15. Index Introduction Search and InformationRetrieval Clustering Visualizationon Mobile Devices Conclusions and Future Works 13

16. 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

17. 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

18. Index Introduction Search and InformationRetrieval Clustering Visualizationon Mobile Devices Conclusions and Future Works 16

20. Compatibilityproblems

21. Screensize

22. Multipleswindowslack

23. Limitednavigation

24. Limitedmemorysize

25. Javascript, cookies

26. Accesibility

27. etc.

28. Solutions:

30. 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

31. Visualizationon Mobile Devices Highresolutioninterface 19

32. Visualizationon Mobile Devices Low resolutioninterface 20

33. Visualizationon Mobile Devices CheckingClusterMed 21

34. Visualizationon Mobile Devices Checkingourproposal Ourproposalobteins at hisfirstversion a 76%

35. Visualizationon Mobile Devices Ourinterface Cancerskin 23

36. Introduction Search and InformationRetrieval Clustering Visualizationon Mobile Devices Conclusions and Future Works 24

38. Itworks!!!

39. FirstMilestone

40. A workingprototype of anInformationretrievalSystemadaptedforMedicalDevicesbasedon Post-retrievalclustering25

42. FocusonusingmedicalontologieslikeUMLS Metathesarusfor:

43. Improvetheclusteringquality

44. Enhance the labelling of the groups

45. Visual help, graph with concepts/cluster relationship26

47. Freebase Ajax connection for search support

48. Bilingual (Spanish-English documents)

49. Put into production (crawling and indexing…) any sponsor at the hall???27

50. 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