1. Improving search efficiency for economic evaluations in major databases using semantic technology Julie Glanville, Carol Lefebvre, Pamela Negosanti, Bill Porter jmg1@york.ac.uk Oct 2010

2. Overview  Why are we interested in economic evaluations?  Can economic evaluations be identified efficiently at present?  This research project  Methods  Results  Discussion  Next steps

3. Why are we interested in economic evaluations?  Systematic reviews and technology assessments frequently consider cost-effectiveness as well as effectiveness outcomes  This information is published in economic evaluations  Cost-effectiveness analyses  Cost-utility analyses  Cost-benefit analyses  Issues in identifying reports of economic evaluations  Poor reporting  abstracts may contain terms which signal an economic evaluation but not an explicit term  Economics is often mentioned in passing in abstracts  Increases number of irrelevant records retrieved

4. Can economic evaluations be identified efficiently?  In healthcare databases  Yes and No  Specific economic evaluation databases are available (NHS EED and HEED)  BUT may need to carry out top up/supplementary searches in large bibliographic databases  Beyond healthcare  Seem to be no economic evaluation databases  Need to search large bibliographic databases such as ERIC and Criminal Justice Abstracts

5. What about search filters?

6. Can search filters help?  In healthcare databases  Many search filters  search filters to find economic evaluations in EMBASE and MEDLINE achieve high sensitivity (100%) (1)  BUT they have poor precision (less than 4%): very high proportion of irrelevant studies are retrieved (1)  Beyond health  Few filters available  Issues of precision likely to be similar to health ( 1)Glanville J, Kaunelis D, Mensinkai S. How well do search filters perform in identifying economic evaluations in MEDLINE and EMBASE. Int J Tech Assess Hlth Care 2009;25:522-529

7. This research project  How can we improve efficiency of retrieval of economic evaluations in large bibliographic databases?  Traditional Boolean approaches don’t seem to be helping  Indexing isn’t very helpful at present  Can semantic analysis software help?  Collaboration with Expert System to explore potential for identifying economic evaluations using their Cogito software

8. Semantic Net

9. Semantic analysis Analysis hat assigns a meaning, a sense, to a syntactic structure and consequently to a linguistic unit, according to the knowledge contained in the semantic network.

10. Methods  Gold standard set of 1950 economic evaluation records (published 2000, 2003, 2006)  identified from NHS EED and then downloaded from MEDLINE.  Comparator set of 4136 matching MEDLINE records for the 3 years (2000, 2003, 2006)  not economic evaluations  But identified using the NHS EED filter  Loaded into Cogito  Divided randomly into test sets and validation sets  Used in-built semantic analysis and also created new rules to categorise economic evaluations to categorise records as economic evaluations or non-economic evaluations

11. Testing and validation Test set 975 economic evaluations 2068 comparator records Validation set 975 economic evaluations 2068 comparator records

12. Results Test set (Gold Standard records=975) (Comparator records = 2068) Validation set (Gold Standard records=975) (Comparator records = 2068) Number of gold standard (GS) records retrieved 975 Number of comparator records retrieved 203385 Sensitivity (number GS retrieved/number of GS records) 100% Precision (number of GS retrieved/number of records retrieved) 82.77%71.69%

13. Results, 2 Precision (combined Test and Validation sets) Sensitivity (combined Test and Validation sets) Using Cogito in-built semantic rules (no filter)77.23%100% Using filter with records scoring 50 78% 90% Using filter with records scoring 10080%85% Using filter with records scoring 20081%83%

14. Discussion  Cogito performs as well as Boolean searching in terms of sensitivity  Cogito has a much improved precision score compared to performance of Boolean filters  Over 70% (Cogito) compared to under 10% (Glanville et al)  Cogito performs well ‘out of the box’  Although early training efforts did not improve precision, further exploration might yield improved results

15. Next steps  Identifying funding to carry out further exploration  Exploring economic evaluation identification optimisation further  Exploring the effects of importing results from a range of databases into Cogito  Exploring whether semantic analysis has potential to achieve improvements in retrieval of other hard to find research where filters do not perform well  diagnostic test accuracy studies and quality of life research  Exploring the potential of semantic analysis for analysing records by study design obtained from a range of databases in h ealthcare, social care, education and criminal justice contexts  in-built rules are database independent.

16. For further information Julie Glanville, York Health Economics Consortium jmg1@york.ac.uk Bill Porter at Expert System http://www.expertsystem.net/ bporter@expertsystem.net