Presentation on integrating structured and unstructured data using the OMOP CDM on the OHDSI platform (www.ohdsi.org) and Regenstrief's NLP tools.

1. Linking Structured and Unstructured
Clinical Phenotypes through the
OMOP Common Data Model
Jon Duke, Charity Hilton, Chris
Beesley, Jonathan Cummins

2. Conflicts of Interest
• None of the authors report any relevant
conflicts of interest

3. Unstructured Data
ICD-9
CPTs Meds
Labs
Structured
Data
EndoscopyPathology
Op Reports
Radiology
Echo’s
Natural
Language
Processing
Traditional
Analytics
Provider Notes

5. Integrated Phenotyping
Combining structured and unstructured data is
optimal for many cohort definitions
Severe CHF: BNP level + NYHA Class

7. Combining Structured and
Unstructured Data is Challenging
• If unstructured data are available, often reside
in a different data environment with different
retrieval mechansims
– e.g., HDFS / Lucene vs RDBMS
• Unstructured text  structured concept
mapping is valuable but may result in data loss
(e.g. no UMLS concept for ‘NYHA 3’)

8. Our Goal
• Seamless integrated cohort generation using
unstructured and structured data sources
• Allow efficient exploration of data in either
environment on cohorts derived from either
source

9. Structured Data:
OMOP CDM v5
www.ohdsi.org

10. Unstructured Data:
HDFS / Solr / UIMA Environment

11. The Magic Sauce
CDM Cohort Table
Solr
Patient List Core
Synchronized Synthetic IDs
and Date Perturbations
Person_id
.
.
.
.
Person_id, cohort_start, cohort_end
.
.
.
.

12. Create in NLP
Send to CDM
Synthetic IDs

13. Create in NLP
Send to CDM

14. Cohort Shows up in CDM
www.github.com/OHDSI/Heracles

15. Can Leverage CDM Tools to Explore

16. Can Leverage CDM Tools to Explore

17. Phase One
• We’ve defined a CDM cohort using text-based
patient identification
• We used a simple search algorithm, but could
extend to NLP pipelines as well (e.g., based on
negation, context, FHx, value extraction)

18. Create in Cohort CDM
Send to NLP
www.ohdsi.org/web/circe

19. Shows up in CDM

20. Shows up in CDM

21. Meanwhile, over in NLP…

22. Import Cohort

23. Pulls Subject IDs from Cohort
Adds to NLP Patient List

24. Available for NLP Search

25. Available for NLP Search

26. Available for NLP Search

27. Phase Two
• We use the CDM Cohort creation tool to build
a cohort based on structured data
• Any CDM cohort can be consumed and
converted into an NLP patient list
• We have thus enabled a JOIN between the
structured and unstructured criteria

28. Conclusion
• Use consistent synthetic identifiers and date-
shifting between CDM and Lucene indices
• Take advantage of APIs from Solr and OHDSI
for cohort insertion
• Provide UI hooks for a consistent and
integrated user experience
• Enable your environment to conduct rich
integrated phenotyping and data exploration

29. Questions?
jonduke@regenstrief.org