Overview of the University of Bath School

1. An overview of the University of Bath School for Health research portfolio in health informatics (Jun 03 – May 05) Dr Maged N Kamel Boulos M.N.K. Boulos @bath.ac. uk

7. Technology alone is not enough to ensure the success of synchronous sessions! We also need to develop the e-skills of synchronous classroom teachers to enable them to use the tools productively.

11. < The BBC Website, Friday 10 September 2004 Findings from our study were widely covered in the news media during September/ October 2004, with a very positive impact on some of the evaluated services, which responded by revising and improving their material.

14. At a glance: geographic information systems Getting the right data sets for GIS analyses can be problematic and costly! Diagrams on this slide are © 2004, 2005 MNK Boulos

16. We recruited a truly international Editorial Board with members from the WHO and US CDC. Quick stats: 61 published papers by authors from around the world as at mid-June 2005 , with more manuscripts in the pipe (under peer reviewing). Current rejection rate: >20%

17. Int J Health Geogr – Impact factor We calculated the number of papers and citations for both Int J Health Geogr (IJHG) and Health & Place , its most directly comparable closed-access journal, which is published by Elsevier. IJHG has, already in its 2 nd year (2003), a higher number of citations per paper than the older Health & Place, and already in its 3 rd year (2004) as many papers published annually. The data are still too scarce on citations to articles published in 2004, but the numbers look very good so far. Hence, IJHG is doing great. 0.6 17 30 2004 HealthPlace 2.1 70 34 2003 HealthPlace 3.8 90 24 2002 HealthPlace 0.7 22 30 2004 IJHG 2.7 27 10 2003 IJHG 1.6 8 5 2002 IJHG per Paper Citations Papers Year Journal Citations Scholar Google

19. The SIG forms part of the Specialist Library in Health Informatics of England's National (electronic) Library for Health (NLH/NeLH). The SIG features a document library, a discussion board, and a forum to share news (including news about forthcoming events), maps and other relevant resources and Web links.

21. It was proposed that EB-GIS 4 HEALTH UK will use the Protégé Ontology Editor and Knowledge Acquisition System. http:// protege .cim3.net/ cgi -bin/ wiki .pl? ProjectsThatUseProtege

29. Colour-blind friendly! Developed using

31. Developed i n collaboration with Graphical Data Capture L td , London, UK

32. Featured in Government Technology , CA, USA, Friday 25 February 2005 http://www. govtech .net/magazine/channel_story. php ?channel=25&id=93172 &quot;Looking for patterns, trends and comparisons in this kind data is crucial for decision makers seeking to tackle public health issues,&quot; said Dr. Maged Boulos, the lecturer in healthcare informatics in the University of Bath's School for Health , who developed the map in collaboration with Chris Russell and Michael Smith from Graphical Data Capture Ltd. &quot;When combined with other data sources and maps, such as demographic, deprivation or social exclusion, transport and existing genito-urinary medicine clinic data sets, the new map could help ministers and public health officials channel their resources and target STD prevention programs to the areas with the most need, or scale such programs according to the magnitude of the problem in different areas.&quot;

38. Online demonstrator: http:// healthcybermap .org/ dermap / Google Images currently offers access to the largest online dermatology image collection (indexed from multiple sources). However, the raw Google Image Search is not very well suited for regional/morphological dermatology searches, hence the need for such an added-value, special regional/morphological search layer/interface to harness Google.

42. The NLH Skin Conditions Specialist Library can be visited at http://www.library. nhs . uk /skin

47. MetaCarta can prove very useful as a bibliometric research tool.

51. Explicit concepts in resource metadata map onto a domain ontology (a clinical terminology or classification) allowing a Semantic Web search engine to infer implicit meanings (synonyms and semantic relationships) not directly mentioned in either the resource or its metadata. Similarly, user queries would map to the same ontology allowing the search engine to infer the implicit semantics of user queries and use them to optimise retrieval.

52. A semantic subject search example. A search for “Borrelia burgdorferi” retrieves resources on “Lyme disease” (ICD-9-CM code: 088.81). Our underlying ICD-9-CM brokering ontology “knows” (or has the relation properly defined or represented in it which says) that “Lyme disease” is-caused-by-> “Borrelia burgdorferi” (an organism).

53. A semantic subject search example (cont’d). A search for “Borrelia burgdorferi” has retrieved this resource metadata record on “Lyme disease” (among other relevant resource records). Note how the words “Borrelia burgdorferi” do not occur in the retrieved resource metadata record shown in this screenshot.