A Multiple Ontology, Concept-Based, Context-Sensitive Search and Retrieval

1. A Multiple Ontology, Concept-Based, Context-Sensitive Search and Retrieval Robert Moskovitch and Prof. Yuval Shahar Medical Informatics Research Center Ben Gurion University, Israel

2. Clinical practice guidelines (CPGs) and protocols are a powerful method for standardizing the quality of medical care The main challenge is providing easy access to CPGs at the point of care Access involves representation of the guidelines and easy, accurate retrieval of relevant guidelines Clinical Guidelines

3. Ben Gurion University’s Digital Electronic Guidelines Library (DeGeL) is an architecture and a Web-based set of computational tools for: Authoring markup (semi-structuring and structuring) Retrieval browsing Runtime application of clinical guidelines Retrospective assessment of the quality of the application The DEGEL Framework

4. Build a search and retrieval tool to retrieve CPGs, to support the challenge of accurately retrieving CPGs at the point of care Enable concept-based search, which supports querying using an existing set of semantic classification indices Support context-sensitive search, which supports querying for a term only within a particular knowledge role (e.g., eligibility conditions) The Goal

5. Classification and Concept Based Search • DeGeL uses seven semantic axes (or aspects) that can categorize CGPs (e.g., diagnosis type, therapy type) • Each axis is implemented as a tree • Each Guideline can be classified under zero, one, or more indices from each axis

6. Plan … Example Markup, Using The Asbru Ontology This guideline is intended only for women who are pregnant and who are at high risk for gestational diabetes and who had a glucose-tolerance test… • Conditions • Filter condition • Setup condition The main goal is reduction of potential hypertension… • Intentions • Outcome intentions • Process intentions The guideline uses mainly dietary measures… If a need for insulin develops, use a guideline for using short-acting insulin… The markup process gradually converts a free-text-based CPG to a semi-structured, then fully structured one, maintaining all formats in parallel (a hybrid architecture)

7. Several ontologies such as Asbru, GEM, and GLIF were developed to represent CPGs in a structured fashion in order to provide automated support for their use Context-Based Search exploits the existence of certain terms within semantically meaningful segments of the text, or knowledge roles Context-Sensitive Search Within Knowledge Roles of Ontologies Example: searching within articles summarizing clinical studies [G.Purcell, 1996]. According to Purcell, a context defines a semantically meaningful region of the document for searching, and thus facilitates precise retrieval of information from the medical literature

8. Document Collection Content Indexing Document Representation Query Formulation Matching Process N : 1 GLS GLM The Information Retrieval Task in the DEGEL Framework Document Collection Information Need ? Query Formulation Content Indexing Matching Process • Vaidurya Query Language- Free Text- Text Value- Text Multiple Value- Int- Date Document Representation Query Representation Retrieved Documents Source Ontology Markup Ontology

9. To implement the Concept-Based Search and the Context-Sensitive Search, two properties for each element in a guideline representation ontology were defined, Search Type and Search Scope. These properties, or aspects, define how an element will be indexed, queried and retrieved. Representing Ontolgies for Search Purposes

10. Search Type

11. Search Scope

12. Query Interface

13. Results Interface

14. The evaluation goals were, to examine the contribution of the concept search and the context sensitive to the traditional full text search. Test sets: TREC NGC CPGs collection Evaluation

15. Concept-based and Context- sensitive evaluation • NGC CPGs collection • 1136 CPGs stored in a GEM based ontology • Classified along two MeSH taxonomies: Disease/Condition and Treatment/Intervention. • Each taxonomy contains ~2500 concepts, in some regions the concepts are 10 levels deep but averages 4-6 levels.

16. In order to evaluate an IR system Queries and judgments should be created. We created a set of 15 daily queries created by 5 physicians ( E&C and Stanford ) Each Physician was asked to label the relevant CPGs, for each query, in the collection. Each query had three formats: Full Text Concept Query in 2nd and 3rd level Context Query in 3 elements Queries and Judgments

17. Evaluation Measures Number of Relevant Documents Retrieved PRECISION = Total Number of Documents Retrieved Number of Relevant Documents Retrieved RECALL = Number of Relevant Documents in the Document Collection

18. Hypothesis 1Retrieval performance will be increased as more context elements are queried, also in addition to full-text search. Hypothesis 2Retrieval performance will be increased as concept based queries will be used in addition to full text search. Evaluation Hypotheses

19. Results – Contextual search

20. Context Sensitive in addition to Full Text search

21. Results – Concept based search in addition to three contexts

22. Results – Concept based search in addition to full-text search

23. Results – Concept based search in addition to single context

24. Concept based search increased the retrieval performance in any of the cases.Improvement observed when deeper queries used using conjunctive relation. Context sensitive search improves performance as more contexts participate in the query. Discussion

25. Questions ?robertmo@bgu.ac.il