So einfach geht modernes Roaming fuer Notes und Nomad.pdf
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
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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
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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
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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
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9. Query modification
• 5713 consecutive query pairs sharing at
least one term, assumed to be single
session
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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
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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
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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
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