The Neighborhood Auditing Tool

1. The Neighborhood Auditing Tool James Geller Yehoshua Perl C. Paul Morrey

2. Dayanand Sagar Kushal Chopra Sandeep Ramachandran Anisa Vishnani Aditi Dekhane Kandarp Shah Rajesh Gupta Suraj Pal Singh Saurabh Patel Kartik Gopal Yakup Kav Rahul Bhave Sirish Motati Pratik Shah Saurabh Singhi Sirish Motati Reddy Sandeep Pasuparthy Ramya Gokanakonda Participating Student Developers 2

3. Overview • Goals of an Auditor’s Tool for the UMLS • Principles of Auditing with Neighborhoods • The Idea of a Hybrid Display • Current State of the NAT: Serving the Auditor • Feature Presentation • Live Audit Session • Planned State of the NAT: Guiding the Auditor • Conclusions and Future Work 3

4. Auditing the UMLS The UMLS consists of over 100 terminologies. It is natural that inconsistencies will appear Over 1.5 million concepts and over 7 million terms Two level structure consisting of the Semantic Network and the Metathesaurus 4

5. How We did it before the NAT: Paper Form CPT: C1081844 Antonospora locustae SRC: NCBI STY: T004T009 Fungus + Invertebrate DEF: SYN: Antonospora locustae | Nosema locustae PAR: Antonospora{STY: Invertebrate} CHD:

6. Previous Work on Auditing • H. Gu, Y. Perl, J. Geller, M. Halper, L. Liu, and J.J. Cimino. Representing the UMLS as an Object-oriented Database: Modeling Issues and Advantages. J Am Med Inform Assoc, 7(1):66-80, 2000. • J. Geller, H. Gu, Y. Perl, and M. Halper. Semantic refinement and error correction in large terminological knowledge bases. Data & Knowledge Engineering, 45(1):1-32, 2003. • Y. Chen, Y. Perl, J. Geller, and J.J. Cimino. Analysis of a study of the users, uses, and future agenda of the UMLS. J Am Med Inform Assoc, 14(2):221-231, 2007. • H. Gu, G. Hripcsak, Y. Chen, C.P. Morrey, G. Elhanan, J.J. Cimino, J. Geller, and Y. Perl. Evaluation of a UMLS auditing process of semantic type assignments. In J.M. Teich, J. Suermondt, and G. Hripcsak, editors, Proc AMIA Symp, pages 294-298, Chicago IL, Nov. 2007.

7. Auditing Results Paper Form (C1081844) Antonospora locustae STY: Fungus + Invertebrate No errors Semantic Type Error: Fungus Semantic Type Error: Invertebrate Ambiguity Add Semantic Type______________________ Other error_____________________________ Comments _____________________________ ______________________________________ 7

8. Goals of an Auditor’s Tool for the UMLS • Display relevant information to the auditor. • Do not overwhelm the auditor with too much information. • Helps the auditor focus on areas most likely to contain errors. • Neighborhood display of reviewed concepts • Algorithms suggest likely erroneous concepts 8

9. Principles of Auditing with Neighborhoods • Several years of experience: Auditing is to a large degree a “local” activity. • Concepts have two kinds of knowledge elements: • Textual Knowledge Elements: Preferred term, CUI, synonyms, LUI, definition, sources, semantic types • CONtextual Knowledge Elements: Neighbors 9

10. Neighborhoods • Focus concept: The concept presently under review • Immediate Neighborhood: The set of concepts reachable from the focus concept by stepping one relationship (up, down, lateral, etc.) • Extended neighborhood: Includes parents of parents (grandparents), children of children (grandchildren) and siblings. No lateral chains. 10

11. Immediate Neighborhood 11

12. Extended Neighborhood 12

13. Up-Extended and Down-Extended Neighborhood • An up-extended neighborhood includes grandparents and the immediate neighborhood. • A down-extended neighborhood includes grandchildren and the immediate neighborhood. • Give auditor all s/he needs but not more.

14. Semantic Type Neighborhood • If we provide the semantic types for every concept, those also form a neighborhood. • It is important to keep the information which semantic types belong to which concepts.

15. References about Neighborhood • M.S. Tuttle, D.D. Sherertz, N.E. Olson, M.S. Erlbaum, W.D. Sperzel, and L.F. Fuller, et al. Using META-1, the first version of the UMLS Metathesaurus. In Proc 14th Annu Symp Comput Appl Med Care, pages 131-135, Washington, D.C., 1990. • S.J. Nelson, M.S. Tuttle, W.G. Cole, D.D. Sherertz, W. D. Sperzel, M.S. Erlbaum, L.L. Fuller, N.E. Olson, From meaning to term: semantic locality in the UMLS Metathesaurus. In Proc Annu Symp Comput Appl Med Care, pages 209-213, Washington, D.C., 1991. • J.J. Cimino, H. Min, and Y. Perl. Consistency across the hierarchies of the UMLS Semantic Network and Metathesaurus. J Biomed Inform, 36(6):450-461, 2003.

16. Desirable Information Beyond Neighborhoods • Concept definition for Focus Concept • Concept sources for Focus Concept • Assigned Semantic Types of concepts • Definitions of relevant Semantic Types • Global view of the Semantic Network • Indented (better for wide branches) • Graphical (better for almost everything else)– we set the standard on this. 16

17. The Idea of a Hybrid Display • Diagrams are wonderful – as long as they fit on one screen. • Indented text is wonderful – as long as there are no or very few multiple parents. • But the UMLS does not fit onto one screen and there are many cases of multiple parents. 17

18. WHAT makes a diagram wonderful? • You can follow parent/child paths with your eyes. • You can get a feeling for everything a concept is connected to with one look. • You can see multiple parents and paths with one look. • You can see global features (short and bushy versus tall and sparse, or (gasp) tall and bushy). 18

19. What makes Indented Text Wonderful? • Indentation expresses parenthood elegantly. • There are no lines crossing. • You don’t need a layout algorithm. • There is a linear order in which to study text. 19

20. The Idea of a Hybrid Display (cont.) • Keep the best features of text and the best features of diagrams. • Maintain relative positions between the focus concept and its children, parents, etc. • Eliminate clutter of arrows. 20

21. A Hybrid Diagram/Form Display of a Neighborhood Parents Synonyms Relationships Focus Concept Children 21

22. Important Auditing Principles • If a concept C has a combination of semantic types assigned, and very few other concepts C1…Cn (n < 6) have that same combination assigned, then C and C1…Cn are suspicious concepts. • We call this “a small intersection.” • Group-based auditing: Audit sets of similar concepts. Y. Chen, H. Gu, Y. Perl, J. Geller, and M. Halper. Structural group auditing of a UMLS semantic type’s extent. J Biomed Inform, 2007. Accepted for publication.

23. Current State of the NAT: Serving the Auditor • The Neighborhood Auditing Tool has been implemented to fully support display of neighborhoods. • Navigation to “adjacent neighborhoods” is easy. • Additional features listed before have been implemented. 23

24. Neighborhood Relationships Siblings Grandparents and grandchildren Synonyms Focus concept definition Focus concept sources Semantic Type display Semantic Type definition Semantic Network (indented) Semantic Network (diagram) Display Options Navigation Search Viewing History UMLS version Demonstration of NAT Features offline version 24

25. Audit Example • An algorithm determined that the concept Antonospora locustae was likely assigned incorrect semantic types. • We follow an auditor’s review of this concept using the data from 2007AA. offline version 25

26. Preliminary Evaluation Study with NAT • Compare paper-based auditing and NAT-based auditing. • Counterbalanced groups. • Recall improves with NAT use. Auditors seem willing to investigate more concepts. • Precision stays the same. Auditors’ mental process does not improve (?).

27. Planned State of the NAT:Guiding the Auditor by Finding (i.e. Computing) Audit Sets • As noted before, errors are likely in small intersections. • Planned new version of the NAT will compute and display small intersections. • Errors are clearly visible in small groups of supposedly similar concepts. • Planned new version of the NAT will compute small groups of supposedly similar concepts. 27

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29. Finding Successively Smaller Groups of Concepts Finding Audit sets by selecting: • Concepts with same semantic type. • Concepts with 1. and same root. • Concepts with 1. and 2. that have the same relationships. 29

31. Audit Set Examples • Example A A selection of concepts in the intersection of Manufactured Object + Organization under the root School (environment). • Example B All concepts that are in a non-chemical intersection with an extent size less than five. 31

32. Possible Auditor’s Recommendations (see Pg. 7) • Mark concept as reviewed and correct. • Mark semantic types that should be removed. • Mark semantic types that should be added. • Mark other kinds of errors. • Attach notes to a reviewed concept. 32

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34. Conclusions and Future Work • Preliminary study showed that people are more successful finding errors with NAT than with paper sources.  • Recall improved with the NAT, precision did not. • NAT seems to nicely complement use of the UMLSKS. 34

35. Conclusions and Future Work (cont.) • This year, work with more human subjects to quantify these observations. • Integration of algorithms for finding audit sets with NAT. • By extent size • Using roots, and relationship patterns within extents. 35

36. Thank you!

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38. Preliminary Evaluation Study

39. Improved Recall • The auditor finds it easy to search for more errors in the neighborhood of the suspicious concept. • With better recall and the same precision you still find more errors.

40. Auditing Demonstration • The concept Antonospora locustae was selected for audit by an algorithm that found it was the only concept assigned to the intersection Fungus + Invertebrate in the UMLS 2007AA. 40

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