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Knowledge-based Information Management for Biomedical Applications

Knowledge-based Information Management for Biomedical Applications. Wesley Chu Computer Science Department University of California Los Angeles, CA wwc@cs.ucla.edu www.kmed.cs.ucla.edu. Outline. Data types Uses of knowledge bases to enhance information management Sample systems

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Knowledge-based Information Management for Biomedical Applications

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  1. Knowledge-based Information Management for Biomedical Applications Wesley Chu Computer Science Department University of California Los Angeles, CA wwc@cs.ucla.edu www.kmed.cs.ucla.edu

  2. Outline • Data types • Uses of knowledge bases to enhance information management • Sample systems • Structured data • Multi-media • Free-text • Conclusion

  3. Information Formats used in Biomedical Applications • Structure Data • Multi-media Images • Semi-structure • Free-text

  4. Uses of Knowledge Bases to Enhance Information Management • Approximate matching • Query conditions • Image features • Similar conceptual terms

  5. Uses of Knowledge Bases to Enhance Information Management • KB query processing • Similarity query answering • Associative query answering • Scenario-specific query answering • Sentinel --Triggering and alerting

  6. Examples of KB Information Systems • CoBase (1990-1998), DARPA • A database that cooperates with the user for structure data • KMeD (1991-2000), NSF • A Knowledge-based medical multi-media database • Medical Digital Library (2001-2005), NIH • A knowledge-based digital file room for patient care, education, and research.

  7. Graduate students:K. Chiang C. Larson R. Lee M. Merzbacher M. Minock Frank Meng Wenlei MaoMark Yang K. Zhang Staff: Q. Chen Gladys ChowHua Yang CoBase www.cobase.cs.ucla.edu • Project leader: Wesley W. Chu

  8. CoBase: Cooperative Databases • Conventional query answering • Need to know the detailed data based schema • Cannot get approximate answers • Cannot answer conceptual queries • Cooperative query answering • Derive approximate answers • Answer conceptual queries • Provide additional relevant answers that user does not (or does not know how to) ask for

  9. CooperativeQueries CoBase Servers Heterogeneous Information Sources Find a nearby friendly airport that can land F-15 Find hospitals with facility similar to St. John’s near LAX CoBase provides: Relaxation Approximation Association Explanation Domain Knowledge Find a seaport with railway facility in Los Angeles

  10. More Conceptual Query Specialization Generalization Conceptual Query Conceptual Query Specialization Generalization Specific Query Specific Query Generalization and Specialization

  11. Cooperative Querying for Medical Applications • Query • Find the treatment used for the tumor similar-to(loc, size)X1 on 12 year-oldKorean males. • Relaxed Query • Find the treatment used for the tumor Class Xon preteenAsians. • Association • The success rate, side effects, and cost of the treatment.

  12. Tumor (location, size) Age Ethnic Group Class X [loc1loc3] [s1 s3] Class Y [locY sY] Preteens Teen Adult Asian African European 11 12 10 9 Japanese Filipino Korean Chinese X3 [loc3 s3] X1 [loc1 s1] X2 [loc2 s2] Type Abstraction Hierarchies forMedical Domain

  13. KB: Type Abstraction Hierarchy • Using clustering technique to group similar • Attribute values • Image features • Spatial relationships among objects • Provides multi-level knowledge (conceptual) representation

  14. Data mining for TAH for NumericalAttribute Values • Clustering metrics: relaxation error • Difference between the exact value and the returned approximate value • Relaxation error is weighted by the probability of occurrence of each value • Can be extended to multiple attributes

  15. Query Display Yes Relax Attribute Answers Database No Query Modification TAHs Query Relaxation

  16. Summary: CoBase • Derive Approximate Answers • Answer Conceptual Queries • Provide Associative Query Answers

  17. Graduate students:Alex Bui Chrisitna Chu John Dionisio T. PlattnerD. Johnson C. Hsu T. Ieong Consultants:Denies Aberle, M.D. C.M. Breant, Ph.D KMeD www.kmed.cs.ucla.edu • PI: Wesley Chu, Ph.D, Computer Science Department • Co-PIs: • A. Cardenas, Ph.D, Computer Science Department • Ricky Taira , Ph.D, School of Medicine

  18. KMeD Goal: Retrieval of Images by Features & Content • Features • size, shape, texture, density, histology • Spatial Relations • angle of coverage, shortest distance, overlapping ratio, contact ratio, relative direction • Evolution of Object Growth • fusion, fission

  19. Characteristics of Medical Queries • Multimedia • Temporal • Evolutionary • Spatial • Imprecise

  20. TAH Lateral Ventricle TAH SR(t,b) TAH Tumor Size TAH SR(t,l) Knowledge Level SR(t,l) SR(t,b) Schema Level Lateral Ventricle Tumor Brain SR: Spatial Relation b: Brain t: Tumor l: Lateral Ventricle Knowledge-Based Image Model Representation Level (features and content)

  21. Queries Query Analysis and Feature Selection Knowledge- Based Query Processing Knowledge-Based Content Matching Via TAHs Query Relaxation Query Answers

  22. User Model To customize users’ interest and preference, needs, and goals. e.g. query conditions, relaxation control, etc. • User type • Default Parameter Values • Feature and Content Matching Policies • Complete Match • Partial Match

  23. User Model (cont.) • Relaxation Control Policies • Relaxation Order • Unrelaxable Object • Preference List • Measure for Ranking • Triggering conditions

  24. Query Preprocessing • Segment and label contours for objects of interest • Determine relevant features and spatial relationships (e.g., location, containment, intersection) of the selected objects • Organize the features and spatial relationships of objects into a feature database • Classify the feature database into a Type Abstraction Hierarchy (TAH)

  25. Similarity Query Answering • Determine relevant features based on query input • Select TAH based on these features • Traverse through the TAH nodes to match all the images with similar features in the database • Present the images and rank their similarity (e.g., by mean square error)

  26. Visual Query Language and Interface • Point-click-drag interface • Objects may be represented by icons • Spatial relationships among objects are represented graphically

  27. Visual Query Example Retrieve brain tumor cases where a tumor is located in the region as indicated in the picture

  28. Summary: KMeD • Image retrieval by feature and content • Matching images based on features • Processing of queries based on spatial relationships among objects • Answering of imprecise queries • Expression of queries via visual query language • Integrated view of temporal multimedia data in a timeline metaphor

  29. Graduate students:Victor Z. LiuWenlei MaoQinghua Zou Consultants:Hooshang Kangaloo, M.D.Denies Aberle, M.D. Medical Digital Librarywww.kmed.cs.ucla.edu • Project leader: Wesley W. Chu

  30. Data Types Used in a Medical Digital Library • Structured data (patient lab data, demographic data,…)--CoBase • Images (X rays, MRI, CT scans)--KMeD • Free-text (Patient reports, Teaching files, Literature, News articles)--FTRS (Free-text retrieval system)

  31. A Free-Text Retrieval System (FTRS) Ad hoc query Knowledge-based Free- Text Retrieval System (FTRS) Patient report for content correlation Query results News Articles Patient reports Medical literature Teaching materials

  32. A Sample Patient Report … Tissue Source: LUNG (FINE NEEDLE ASPIRATION) (LEFT LOWER LOBE) … FINAL DIAGNOSIS: - LUNG NODULE, LEFT LOWER LOBE (FINE NEEDLE ASPIRATION): - LUNG CANCER, SMALL CELL, STAGE II. … … Tissue Source: LUNG (FINE NEEDLE ASPIRATION) (LEFT LOWER LOBE) … FINAL DIAGNOSIS: - LUNG NODULE, LEFT LOWER LOBE (FINE NEEDLE ASPIRATION): - LUNG CANCER, SMALL CELL, STAGE II. …

  33. ??? How to treat the disease ??? How to diagnose the disease Diagnosis-related articles Treatment-related articles Scenario-Specific Retrieval … Tissue Source: LUNG (FINE NEEDLE ASPIRATION) (LEFT LOWER LOBE) … FINAL DIAGNOSIS: - LUNG NODULE, LEFT LOWER LOBE (FINE NEEDLE ASPIRATION): - LUNG CANCER, SMALL CELL, STAGE II. …

  34. Challenge I: Indexing for Free-Text • Extracting key concepts in the free-text for indexing • Free-text: Lung cancer, small cell, stage II • Concept terms in knowledge source: stage II small cell lung cancer • Conventional methods use NLP • Not scalable

  35. Challenge II: Mismatch between terms used in query and documents • Example Query: … lung cancer, … ? ? ? Document 1: … lung carcinoma … Document 3: anti-cancerdrug combinations… Document 2: … lung neoplasm …

  36. ? √ Challenge III: Terms used in the query are too general Expanding the general terms in the query to specific terms that are used in the document Query: lung cancer, diagnosis options Query: lung cancer, chest x-ray, bronchography, … Document: … the effectiveness of chest x-ray and bronchography on patients with lung cancer …

  37. A Medical KB:Unified Medical Language System (UMLS) • Meta-thesaurus - control vocabulary (1.6M biomedical phrases, representing 800K concepts) • Semantic Network – classify concepts into classes (e.g. disease and syndrome, treated by, therapeutic procedure, etc.) • Specialized Lexicon

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