Presentation on theme: "Linked Data and Cochrane Reviews A report from the ‘Star Trek’ Crew Chris Mavergames Web Operations Manager/Information Architect Cochrane Collaboration."— Presentation transcript:
Linked Data and Cochrane Reviews A report from the ‘Star Trek’ Crew Chris Mavergames Web Operations Manager/Information Architect Cochrane Collaboration Web Team
Structure of this talk Intro to linked data and what it means for Cochrane "Star Trek" stream of work so far What's possible now and in the future * Acknowledgements to Lorne Becker and the entire Star Trek crew. Their input was invaluable in the preparation of this talk.
Cochrane Reviews are fantastic BUT… There are problems that limit their use by some people ◦Difficult to wade through all of the text ◦Difficult to understand the figures, terminology, and other bits of the Review ◦Hard to compare interventions without reading multiple Reviews ◦Can be difficult to find the Review you seek
Searching The Cochrane Library Search for “Prozac” – no reviews Search for “fluoxetine” – 25 reviews
Ideally we’d restructure our content for different users Beginning to do this now: ◦Summaries.Cochrane.org for consumers ◦Cochrane Clinical for clinicians BUT ◦Takes a lot of work to reformulate reviews & authors, CRGs, etc are busy Wouldn’t it be nice if we could automate or partially automate this?
How did Bing read 3 different weather sites & bring me the data I need?
Could we do similar magic with our Cochrane reviews? If so, what might we be able to accomplish?
Machines aren‘t good at reading web pages Data on the web is meant for human consumption Machines need the data to be structured Once structured, information can be more easily shared within datasets and across web pages
Links to the CRS Lack of unique study IDs a real problem CRS solves this by providing a unique ID for all studies that can be referenced Better linking of data about trials and possibilities with linking to external sources such as PubMed (example later)
Linked data technologies OWL (Web Ontology Language) RDF (Resource Description Framework) SPARQL (RDF query language) Model Cochrane Reviews in OWL Transform them into RDF and add to triple store Query them with SPARQL OR, simply...
We can… Ask questions that use data from several different reviews Enhance the experience of our users by including data from the triple stores of others Improve search Make it easier for people to find Cochrane Reviews
Ask questions that use data from several different reviews Enhancing the User Experience
A question using multiple reviews I’ve done a search for trials on a particular intervention for dementia. I want to know which of the trials have been included in a Cochrane Review.
Finding the answer the old way Search for the relevant Reviews Read the reference lists to find included trials Compare with my trial search Eliminate the new references that are additional publications from trials already included in a Review. OR…
The “Star Trek” Way My list of trials A Machine Generated list of trials not yet included in a review The Cochrane Review “Triple Store” A ”studified” list from the CRS
Links to the relevant Review for those trials that were included
INSERT IMAGE FOR QUESTION 1 HERE Question 1: SPARQL query and partial list of results
Another question using multiple Reviews What are the risks of bias for the entire set of trials assessing the effectiveness of a particular intervention?
Finding the answer the old way Search for the relevant reviews (there may be more than one) Read the tables of included studies to find risk of bias assessments for each trial Combine them * (in some cases review authors may have done this for all of the trials in a single review)
The “Star Trek” Way A Machine generated summary of the Risk of Bias assessments for the relevant trials The Cochrane Review “Triple Store”
Question 2 visualized These figures summarize Risks of Bias from the trials included in the reviews in your search RoB Summary for Cochrane Reviews on dementia
Cochrane Reviews marked up in semantic markup can be linked to news publishers For example, BBC Health writers could be suggested related Cochrane evidence for a particular story they are writing And, could include a link to primary source material such as a Cochrane Review Thus driving traffic to our Reviews
Making our content nimble Structured and linked data can help make our content “nimble” Nimble content can: Travel Freely Retain Context Meaning Create New Products - R. Lovinger, Razorfish
Structured data "Structured data allows you to preserve your value proposition over a longer distance to a much wider audience." - Martin Hepp, creator of the Good Relations ontology
Incremental development Implementing semantic and linked data technologies should be: Non-invasive Agile Low impact (on staff – hopefully, high impact on users!)
Looking to the future What would Cochrane data “look like” outside of it’s container, the Review?
Risk of Bias in PubMed For example: someone who is looking at a study in PubMed might be interested in seeing Cochrane’s Risk of Bias assessment of this study, regardless of whether they are interested in the overall Cochrane Review that includes that study.
Summary Linked Data or Web 3.0 is here How can we leverage these tools to further our mission Requires that we think differently about the “container“ of the Review Our data needs to become “nimble“ to meet future user needs We should proceed slowly, incrementally What are the “quick wins“ – Links to CRS? Across-Review queries? Links to external datasets
EbHC Semantic Platform CDSR CRS/ CENTRAL DARE HTAs CMR
EbHC Semantic Platform CDSR CRS/ CENTRAL DARE HTAs CMR Drug Bank UMLS Diseasome Symptom Ontology * BBC Health Ontology * Not yet created
Your consent to our cookies if you continue to use this website.