This document describes enabling semantic search of bio-specimen data from multiple repositories at OHSU. It discusses using entity recognition and mapping to SNOMED-CT to semantically annotate pathology reports. An OWL ontology was developed with subclass and part-of relationships to support description logic queries over the annotated concepts. This allows searching for anatomical sites associated with specific pathologies or diseases. Challenges include the structured-text format of reports and limitations of current natural language processing and ontology techniques.

1. Enabling semantic search
in a bio-specimen
repository
July 9th, 2013
ICBO 2013
Shahim Essaid, Carlo
Torniai, and Melissa Haendel

2. OHSU’s Biolibrary Search
Engine
 Data aggregated from four repositories
with plans for additional repositories
 A web-based search engine over de-
identified data
 Our goal was to develop a controlled
application ontology to support search
capabilities

3. OHSU Biolibrary system

4. Search application
Two search interfaces
(with no data integration)
Limited free text
search

5. Search application
Search through anatomy and
histology lists
Multiple wizard-like
forms

6. Example coded data vs. pathology
report
(Available structured data from one case)
However, pathology report also includes:
• Low grade pancreatic intraepithelial neoplasia
• Extensive perineural invasion
• Acute and chronic cholecystitis
• Bile duct tissue with chronic inflammation
• Chronic pancreatitis
• Acute gastric serositis

7. Entity recognition with
MetaMap

8. Selected mapping examples
(the same report from earlier)
Final Pathologic Diagnosis:
A: Gallbladder, cholecystectomy:
- Acute and chronic cholecystitis
- Negative for malignancy
B: Bile ductular tissue, biopsy:
- Bile duct tissue with chronic
inflammation
- Negative for malignancy

9. C: Superior mesenteric vein
margin, biopsy:
- Vascular tissue with no
diagnostic abnormality
- Negative for malignancy
D: Portal vein margin, biopsy:
- Fibroconnective tissue with no
diagnostic abnormality
- Negative for malignancy
Selected mapping examples
(the same report from earlier)

10. Selected mapping examples
(the same report from earlier)
E:
Pancreas, stomach, duodenum, pancreaticogast
roduodenectomy:
- Pancreatic ductal adenocarcinoma, grade
2/3, invading peripancreatic fat
- Size: 3 cm in greatest dimension
- Pancreatic neck margin positive for invasive
carcinoma (please see comment)
- Superior mesenteric artery margin negative
at 0.2 cm from invasive tumor, deep
pancreatic margin negative at 0.6 cm from
invasive tumor
- Extensive perineural invasion present
- No angiolymphatic invasion identified
- Metastatic pancreatic ductal
adenocarcinoma present in two of ten
peripancreatic lymph nodes (2/10)

11. Deriving an OWL ontology for DL
queries

12. Adding relationships
(developing an application ontology to support
search)
 “subclass of” axioms generated based on the UMLS hierarchy
table
 Mapped entities were augmented with transitive closure of
parents
 “part of” axioms were generated by aggregating many
mereological relationships from the UMLS relationship table
 Relate anatomy, pathology, and disease entities with
SMOMED-CT disorder/disease definitions

13. Adding relationships
(developing an application ontology to support
search)
 Problematic multiple and cyclic inheritance resolved manually
 Resulted in an OWL ontology that supports useful DL queries
along the “subclass of” and “part of/has part” axes. Examples:
• Retrieve all pathologies (limited to a type if needed) that
affect an anatomical site (± all parts)
• Retrieve all anatomical sites with a specific type of pathology
• List all pathologies/sites for a disease
• Etc.
 The MetaMap mappings were saved in a database table. After
relevant concepts are identified with a DL query, a database
query can find actual reports.

14. SNOMED-CT examples of disorder definitions
(used to relate anatomy to pathology in the application ontology)

15. Application integration
 Integration with existing application was limited to
appending the annotations to the text of pathology
reports
| C1521733 C0332144 0:26 | C0016976 32:44 |
C0205178 63:70 | …
 Annotations (CUIs and location) are then indexed in
Solr and can be searched with the existing free text
search form. (after a DL query on the OWL file)

16. A simple DL query for anatomy
(linked to actual report in the mapping table)

17. Difficulties and limitations
 “Structured” text in pathology reports is not in natural
language, making it perform less well using MetaMap
 Named entity recognition helps with document retrieval
but extraction of structured data is more valuable
 Negation detection is poor but very important
 Significant multiple inheritance and subsumption cycles
(inappropriate equivalences) when several UMLS
vocabularies are used to derive an OWL representation
 Short project, no access to full reports, limited
computational resources

18. Conclusions
 OHSU Biolibrary is adding many other specimen
collections, need for better search will increase
 Can use NER to enhance the data with SNOMED-CT
 Interest in identifying references in pathology reports
to specimen blocks and slides to annotate these
resources as well
 Still limited resources for supporting sophisticated
terminology and semantic efforts….

19. Thanks
 Dr. Chris Corless
 Rob Schuff
 Medical Research Foundation of Oregon