WP 3.1 Training

1. WP 3.1 Training • Marc Weeber • Johan van der Lei

2. Overview • Inventory/Analysis of current situation • Ideal curriculum? • Database of courses

3. Inventory

4. Inventory • First list of worldwide biomedical training programs available (IMIM, ISCIII) • Low response of NoE partners to request for information • New approach: personal contact with partners. May be “force” them by making them interview candidates

5. Start with... • Analysis of programs/courses provided by partners • Analysis of important European, non-NoE, institutes (suggestions, please) • Analysis of most important US-based integrated programs (list provided by ISCIII): Stanford, Oregon (OHSU), Columbia

6. Analysis • Analysis of programs and courses • Focus on classification MI, BI, and integrated BMI (required for gap analysis) • ACMI: true integration of informatics theory with biomedical subjects on a per course base • Focus on multi-disciplinary approach

7. Procedure • ERASMUS will contact NoE partners with personal emails with request for information • ERASMUS/(F)IMIM will do the analysis • TMC will see drafts of the reports • First draft anticipated in August

8. Ideal Curriculum?

9. Ideal Curriculum? • ACMI report stresses integration, however, no indication of an ideal curriculum is provided • What is the Network’s view on an integrated biomedical curriculum. • Interview key people in the Network. May be 1 or 2 US key players (Authors of ACMI report)?

10. Procedure • ERASMUS/(F)IMIM will develop interview protocol • ERASMUS/(F)IMIM will propose interviewees • TMC will approve protocol and interviewees • Interviews will (mostly) be held at Edinburgh meeting

11. Interviews • Description of current situation • How integrated are MI and BMI? • Perceived shortcomings • Ideas for better integration • Integration in teaching should be based on integration in research: is this available at the institute?

12. Development of Scenario’s • Based on interviews: make small list of likely scenario’s of well-integrated BI and MI curricula • Different levels of integration are possible • Interviewees will comment on this (email/phone) • Interviewees will point out gaps with current situation

13. Database of Courses

14. Database • MI, BI, and BMI courses and programs • Manually curated and (semi-)automatically entered data • Inspiration: http://www.healthtraining.org

17. DEGREE MSc, PhD, MA, Postdoctoral DURATION Semester, summer/winter school, year METHOD theory, practical (lab) EXAMINATION Exams, essay, experiments, programming project INSTITUTE Erasmus MC, IMIM, Stanford COUNTRY The Netherlands, Spain, USA TOPIC Thesaurus with topics Taxonomy/Thesaurus • Different Axes:

18. TOPIC Thesaurus • Re-use existing lists/taxonomies/thesauri. Anyone? • Term-based (1, 2, and 3 words) frequency analysis of existing descriptions of MI, BI, and BMI programs and courses (Based on IMIM list) • Flat list or hierarchy? Expansion of search terms with children?

19. Language • Include English-only courses? • Endorse English courses throughout the NoE? Improves mobility?

20. Procedure Thesaurus • TMC will suggest thesauri/taxonomies of course classifications, if available • ERASMUS will make list of possible thesaurus terms based on frequency analysis • TMC will evaluate terms • ERASMUS will make final list/hierarchy • TMC approves thesaurus • Thesaurus ready: August

21. Manual Curation • Members can enter their programs and courses into the database using text or a URL • Manually assign topics or use computer-suggested indexing terms • Problem of updating? Healthtraining.org seems well-curated and up-to-date

22. Collexis Indexing and Retrieval

23. Erasmus MC and Collexis • Original technology developed at Erasmus MC, commercialized by Collexis • Erasmus MC has unlimited use of software • Collexis has first right of commercial exploitation of Erasmus MC’s newly developed tools • In practice: R&D is interconnected

25. Collexis Fingerprints • Collexis is based on the principle of “fingerprinting” • Fingerprint: profile of a piece of information • A fingerprint contains a list of weighted concepts • Concepts are derived from a thesaurus • Fingerprint characteristics: unique and small

26. Example Fingerprint

27. Indexing Concept matching Word-based searching What? Where? Who? Indexing Defining a Search

28. Means of Transportation Aircraft Airplane Train Motor Vehicle Plane Truck Automobile Lorry Car Simplified Thesaurus

29. Email Word papers books RFP’s CVs skills jobs Organization Content People Aggregation of Fingerprints

30. Search Query or Document Selected Concepts Query/Document Fingerprint This is a lot of text that is about nothing much more than talking about how much text this is.This is a lot of text that is about nothing much more than talking about how much text this is.This is a lot of text that is about nothing much more than talking about how much text this is.This is a lot of text that is about nothing much more than talking about how much text this is.This is a lot of text that is about nothing much more than talking about how much text this is.This is a lot of text that is about nothing much more than talking about how much text this is.This is a lot of text that is about nothing much more than talking about how much text this is.This is a lot of text that is about nothing much more than talking about how much text this is.This is a lot of text that is about nothing much more than talking about how much text this is.This is a lot of text that is about nothing much more than talking about how much text this is.This is a lot of text that is about nothing much more than talking about how much text this is.This is a lot of text that is about nothing much more than talking about how much text this is.This is a lot of text that is about nothing much more than talking about how much text this is.This is a lot of text that is about nothing much more than talking about how much text this is.This is a lot of text that is about nothing much more than talking about how much text this is.This is a lot of text that is about nothing much more than talking about how much text this is. Removing Stop Words Specificity Concept Concept Concept Concept Concept Concept Concept Concept Concept Concept Concept Concept Concept Concept Concept Concept Concept Concept Concept Concept Concept Concept Concept Concept Concept Concept Concept Concept Concept Concept Concept Concept Concept Concept Concept Concept Concept Concept Similarity Normalization Frequency Concept Lookup Clustering Indexing

31. Concept 1 D3 D1 Query Concept 1 D2 Concept 3 Vector Space Model

32. Set up of Database

33. Student’s Perspective • Two search modes: using taxonomy or free text • Two search areas: programs and courses • Free text can be restricted with DEGREE, EXAMINATION, etc. fields

34. Institute’s Perspective • Web site to update information • Entering text or URL • Manual assignment of TOPICs or computer assistance • Automatic reminders when information “expires”

35. Procedure Database • ERASMUS will develop Collexis-based database • TMC/(F)IMIM will provide functional and graphical interface requirements • ERASMUS will develop web interface • TMC will see several prototypes