Supporting Evidence Based Medicine Sponsors: Mala Sarat ......Sponsors: Mala Sarat Chandra, Mike...
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OKDoctor Validity Potential Rating Research Ranked by Validity Evaluation Model Extraction Ontology Concept Extraction Module EVIDENCE BASED MEDICINE EZ-ETL Evaluation Model New Derived Information Extraction Ontology Concept Extraction Module INTRANET DOCUMENTS TOP SECRET INTELLIGENCE REPORTING NATIONAL SECURITY HUMAN RESOURCES FINANCE Application Based on Domain Ontology EZ-ETL EZ-ETL -> OKDOCTOR Ontology-Driven Data Analysis Supporting Evidence Based Medicine Vijay Gaikwad and Tom Vandermolen Sponsors: Mala Sarat Chandra, Mike Doane Evidence Based Medicine (EBM) requires practitioners to assess the quality of pub- lished medical research for relevance and va- lidity to their specific field. However, there is an overwhelming volume of research being published, and evaluating research requires careful reading of each report. How can practitioners keep up? How can they identify and use quality research? Problem: Evaluating Evidence For Evidence Based Medicine 3,000 Research articles published on PubMed every day Existing solutions provide either too much or not enough information. OKDoctor complements both approaches by providing an evaluation metric--the Validity Potential Ranking--to help EBM practitioners quickly find the evidence with the highest validity. Solution: OKDoctor The Validity Potential Ranking: - Rates whether the article has the earmarks of a valid study - Helps practitioner find most valid documents The Validity Potential Rating is comprised of 3 sub-ratings, each representing different aspects of EBM standards. Document Validity Study Validity VPR Information Currency This approach can be used for any general data problem, with any type of text-based data--the only variable is the Domain Ontology. A PLATFORM SOLUTION Data Sources Knowledge Domains An ontology-driven Extract, Transform, Load (ETL) process, EZ-ETL combines Semantic Web and Machine Learning techniques to extract concepts from any text data source and translate them into concepts specific to the customer knowledge domain. - Uses a customer-provided Domain Ontology as a “schema” to structure unclean data sources - The same customer-based domain ontology drives: -- Back-end data processing -- Front-end user application - Allows re-use of existing rich ontologies Solution: EZ-ETL Problem: Cleaning “Dirty” Data Big Data = Big Problems - Unstructured, “dirty” data is everywhere - No formalized method to turn unstruc- tured data into useful structured data - Process is repeated for every problem - Very difficult to automate the process of finding relationships and structure in textual data The Extract, Transform, Load (ETL) process is usually still carried out MANUALLY 80% of a data scientist’s time is spent preparing the data before analysis EZ-ETL Applied to the Domain of Evidence Based Medicine Future Work: - More advanced Natural Language Processing - Enhance relevance and improve extraction accuracy - Expert feedback on VPR equation and factors - Incorporate more complex Machine Learning techniques - Incorporate more advanced Natural Language Processing - Exploit the network of relationships in the Domain Ontology Future Work: So Me Lab Social Media Lab @ UW Technology and Assistance Provided by:
Transcript of Supporting Evidence Based Medicine Sponsors: Mala Sarat ......Sponsors: Mala Sarat Chandra, Mike...
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OKDoctorValidityPotentialRating
ResearchRanked by Validity
Evaluation Model
Extraction Ontology
Concept ExtractionModule
EVIDENCE BASEDMEDICINE
E Z - E T L
Evaluation Model
NewDerived
Information
Extraction Ontology
Concept ExtractionModule
INTRANETDOCUMENTS
TOP SECRET
INTELLIGENCEREPORTING
NATIONAL SECURITYHUMAN RESOURCES
FINANCE
ApplicationBased on Domain
Ontology
E Z - E T L
EZ-ETL -> OKDOCTOR Ontology-Driven Data Analysis Supporting Evidence Based Medicine Vijay Gaikwad and Tom VandermolenSponsors: Mala Sarat Chandra, Mike Doane
Evidence Based Medicine (EBM) requires practitioners to assess the quality of pub-lished medical research for relevance and va-lidity to their speci�c �eld. However, there is an overwhelming volume of research being published, and evaluating research requires careful reading of each report.
How can practitioners keep up? How can they identify and use quality research?
Problem: Evaluating Evidence For Evidence Based Medicine
3,000 Research articlespublished on PubMed every day
Existing solutions provide either too much or not enough information. OKDoctor complements both approaches by providing an evaluation metric--the Validity Potential Ranking--to help EBM practitioners quickly �nd the evidence with the highest validity.
Solution: OKDoctor
The Validity Potential Ranking:- Rates whether the article has the earmarks of a valid study- Helps practitioner �nd most valid documents
The Validity Potential Rating is comprised of 3 sub-ratings,each representing di�erent aspects of EBM standards.
DocumentValidity
StudyValidity
VPR
InformationCurrency
This approach can be used for any general data problem, with any type of text-based data--the only variable is the Domain Ontology.
A PLATFORM SOLUTION
Data Sources
Knowledge Domains
An ontology-driven Extract, Transform, Load (ETL) process, EZ-ETL combines Semantic Web and Machine Learning techniques to extract concepts from any text data source and translate them into concepts speci�c to the customer knowledge domain.
- Uses a customer-provided Domain Ontology as a “schema” to structure unclean data sources - The same customer-based domain ontology drives: -- Back-end data processing -- Front-end user application - Allows re-use of existing rich ontologies
Solution: EZ-ETL
Problem: Cleaning “Dirty” DataBig Data = Big Problems - Unstructured, “dirty” data is everywhere - No formalized method to turn unstruc-tured data into useful structured data - Process is repeated for every problem - Very di�cult to automate the process of �nding relationships and structure in textual data
The Extract, Transform, Load (ETL) process is usually still carried out MANUALLY
80% of a data scientist’s timeis spent preparing the data before analysis
EZ-ETL Applied to the Domainof Evidence Based Medicine
Future Work:- More advanced Natural Language Processing - Enhance relevance and improve extraction accuracy- Expert feedback on VPR equation and factors
- Incorporate more complex Machine Learning techniques- Incorporate more advanced Natural Language Processing- Exploit the network of relationships in the Domain Ontology
Future Work:
SoMe LabSocial Media Lab @ UW
Technology and Assistance Provided by:
