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Log Analysis to Understand Medical
  Professionals' Image Searching
            Behaviour
Theodora Tsikrika
Henning Müller
Charles E. Kahn
Overview
•   Medical image retrieval
•   Motivation of our work
•   Methods
•   Log file analysis
•   Search strategies
•   Frequent information needs
    •   Use as topics for a retrieval benchmark
•   Conclusions

                                                  2
Medical image retrieval
 •   Medical professionals frequently and
     increasingly search for visual information
     (images, videos)
 •   Particularly radiologists often search for
     images
     •   Internet search increasingly replaces search in
         reference books and discussions with colleagues
     •   Images are important for differential diagnosis,
         finding explications for unclear visual patterns
 •   Different types of image search systems
     •   Text-based search for images
                                                            3
Motivation
•   Knowing search tasks, goals and formulations
    of user groups for information retrieval is
    important
    •   To build new IR systems or benchmark existing ones
•   Several surveys have been performed
•   Log file analyses were done as well
    •   MedLine log files, not really for images
    •   HONmedia search, less focused as not radiologists, but
        rather general public, health professionals
•   Image search on the Internet for radiologists
    has increased strongly
                                                                 4
Log file analysis
 •   Session level, query level, term level
 •   Search logs have received much attention to
     learn more on user behavior
     •   Bad example: release of AOL log, privacy!!
     •   Amount of information differs, IP addresses, time
         stamps
 •   Session level is interesting as much is
     learned on behavior, query modifications,
     even satisfaction
     •   Terms added, removed, changed?
 •   Query and term level often focus on                     5
Methods
•   ARRS Goldminer made a log file available
    •   25’000 consecutive searches of medical
        professionals
    •   Search system is very popular with radiologists
        •   Allows search terms, selection of gender, age and
            modality

•   Search term normalization
    •   All lower case, removing special characters, quotes
    •   Manual work: “xray”, “x-ray”, “x ray” all equals “xray”
•   Removal of identical consecutive queries
•   No time stamps available, no IP address                       6
Results of the analysis
 •   23’033 queries after preprocessing, 14’413 of
     these are unique queries (63%)
 •   Query length 2.24 words, 2.46 for unique
     queries
     •   Similar to web search, one term less than MedLine
 •   Imaging modalities:
     •   MRI (586), CT (425), ultrasound (199), xray (139),
         PET (34), PET/CT (13), angiography (13), echo (11),
         radiography (10), tomography (6), fMRI (3), PET/MRI
         (1)
     •   This despite the possibility to filter for modalities
                                                                 7
Most frequent queries and terms




                                  8
Query modification
 •   5713 consecutive query pairs sharing at
     least one term, assumed to be single
     session




                                               9
Use of terms for topics in
ImageCLEF
 • ImageCLEF, image retrieval benchmark
    •   Using images and text as queries, 17 groups
        participated in 2012
•   Taking most frequent searches, at least two
    terms
•   Radiologist ranked these search terms by
    usefulness in radiology
•   Most useful terms were checked to find
    whether documents in PubMedCentral fulfill
    the need
•   30 most useful, most frequent, available
                                                      10
Conclusions
 • Analysis of log files can help understand
   user behavior
     •   Help build better systems based on user models
         and analyze current approaches, also
         shortcomings
 •   Time stamps and user identification are
     important for query session analysis
     •   We used implicit knowledge for this
 •   People do not know all details of systems
     •   Search for modalities in text and through filters
 •   Depending on results, users change terms
                                                             11
Questions?
 •   More information can be found at
     •   http://www.khresmoi.eu/
     •   http://medgift.hevs.ch/
     •   http://publications.hevs.ch/




 •   Contact:
     •   Henning.mueller@hevs.ch
                                        12

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Understanding Medical Image Search Behavior Through Log Analysis

  • 1. Log Analysis to Understand Medical Professionals' Image Searching Behaviour Theodora Tsikrika Henning Müller Charles E. Kahn
  • 2. Overview • Medical image retrieval • Motivation of our work • Methods • Log file analysis • Search strategies • Frequent information needs • Use as topics for a retrieval benchmark • Conclusions 2
  • 3. Medical image retrieval • Medical professionals frequently and increasingly search for visual information (images, videos) • Particularly radiologists often search for images • Internet search increasingly replaces search in reference books and discussions with colleagues • Images are important for differential diagnosis, finding explications for unclear visual patterns • Different types of image search systems • Text-based search for images 3
  • 4. Motivation • Knowing search tasks, goals and formulations of user groups for information retrieval is important • To build new IR systems or benchmark existing ones • Several surveys have been performed • Log file analyses were done as well • MedLine log files, not really for images • HONmedia search, less focused as not radiologists, but rather general public, health professionals • Image search on the Internet for radiologists has increased strongly 4
  • 5. Log file analysis • Session level, query level, term level • Search logs have received much attention to learn more on user behavior • Bad example: release of AOL log, privacy!! • Amount of information differs, IP addresses, time stamps • Session level is interesting as much is learned on behavior, query modifications, even satisfaction • Terms added, removed, changed? • Query and term level often focus on 5
  • 6. Methods • ARRS Goldminer made a log file available • 25’000 consecutive searches of medical professionals • Search system is very popular with radiologists • Allows search terms, selection of gender, age and modality • Search term normalization • All lower case, removing special characters, quotes • Manual work: “xray”, “x-ray”, “x ray” all equals “xray” • Removal of identical consecutive queries • No time stamps available, no IP address 6
  • 7. Results of the analysis • 23’033 queries after preprocessing, 14’413 of these are unique queries (63%) • Query length 2.24 words, 2.46 for unique queries • Similar to web search, one term less than MedLine • Imaging modalities: • MRI (586), CT (425), ultrasound (199), xray (139), PET (34), PET/CT (13), angiography (13), echo (11), radiography (10), tomography (6), fMRI (3), PET/MRI (1) • This despite the possibility to filter for modalities 7
  • 8. Most frequent queries and terms 8
  • 9. Query modification • 5713 consecutive query pairs sharing at least one term, assumed to be single session 9
  • 10. Use of terms for topics in ImageCLEF • ImageCLEF, image retrieval benchmark • Using images and text as queries, 17 groups participated in 2012 • Taking most frequent searches, at least two terms • Radiologist ranked these search terms by usefulness in radiology • Most useful terms were checked to find whether documents in PubMedCentral fulfill the need • 30 most useful, most frequent, available 10
  • 11. Conclusions • Analysis of log files can help understand user behavior • Help build better systems based on user models and analyze current approaches, also shortcomings • Time stamps and user identification are important for query session analysis • We used implicit knowledge for this • People do not know all details of systems • Search for modalities in text and through filters • Depending on results, users change terms 11
  • 12. Questions? • More information can be found at • http://www.khresmoi.eu/ • http://medgift.hevs.ch/ • http://publications.hevs.ch/ • Contact: • Henning.mueller@hevs.ch 12