This document discusses automating the handling of clinical terms using SNOMED CT description logic. It presents an algorithm that accepts clinical terms from users and processes them behind the scenes into machine-readable SNOMED CT codes. These codes are then analyzed to derive relationships and attributes, and presented back to users in human-readable form. Screenshots demonstrate the ability to process different clinical data types such as findings, diseases, and investigations. The goals are to handle clinical data in coded form behind the scenes while accepting and presenting data to users in plain language.

1. AUTOMATED HANDLING OF
CLINICAL TERMS USING
SNOMED CT DESCRIPTION
LOGIC
Dr SB Bhattacharyya
MBBS, MBA, FCGP
Member, National EHR Standardisation Committee, MoH&FW, GoI
Member, Health Informatics Sectional Committee, MHD 17, BIS
Hony. State Secretary (2015), IMA Haryana
President (2010 – 2011), IAMI

2. WHAT USER ENTERS/GETS CAPTURED
FROM UI
Pneumonia
Dr SB Bhattacharyya© 2

3. WHAT GETS STORED IN THE DATABASE
233604007
[may require post-processing]
OR
=== 95436008+205237003:{116676008=707496003,363698007=39607008}
[is pre-processed]
Dr SB Bhattacharyya© 3

4. WHAT IS DERIVED ON PROCESSING
• Relations:
• 95436008
• 205237003
• Attributes
• 116676008=707496003
• 363698007=39607008
Dr SB Bhattacharyya© 4

5. WHAT GETS PRESENTED TO USERS
• Any one of the following
• Relations
• Lung consolidation (disorder)
• Pneumonitis (disorder
• Attributes
• Associated morphology (attribute) is Inflammation and consolidation (morphologic
abnormality)
• Finding site (attribute) is Lung structure (body structure)
• Pneumonia (requires additional algorithmic steps)
Dr SB Bhattacharyya© 5

6. SCREENSHOT OF PROGRAMME
DEMONSTRATING THE ABILITY OF
AUTOMATION TO PROCESS CLINICAL
TERMS USING SNOMED CT
NB : ONLY THE TERM WAS ENTERED, NOTHING ELSE
Dr SB Bhattacharyya© 6

7. Dr SB Bhattacharyya© 7

8. ASSUMPTIONS
• One uses SNOMED CT for its
• Concept model that permits a single term to be analysed in a myriad of ways
• Having a term for almost the entire clinical spectrum from abscess to zygote
• Plenty of synonyms for each term
• Uses machine processable codes
• The above are taken into consideration and any term is supplied to the
programme to convert into machine-processable codes that are then analysed in
the background and inform the user in human-readable terms the various
components that may then be queried
Dr SB Bhattacharyya© 8

9. ASSUMPTIONS [CONTD.]
• Only pre-coordinated expressions are handled
• Although very much possible to perform the required tasks, due to underlying
programming complexities the algorithm does not take into consideration the
following
• Relationship groups in the inferred view of the term
• Getting back the original term that was supplied by the user
• Ability to locate crucial and meaningful concepts within the terms as revealed
through the concept model is not significantly impacted
Dr SB Bhattacharyya© 9

10. AIMS & OBJECTIVES
• To demonstrate the ability of automation to process clinical data in machine-
processable coded form in the background and accept from/present back to the
user human-readable terms
• Users may enter and/or query clinical data in human-readable form
Dr SB Bhattacharyya© 10

11. AIMS & OBJECTIVES
• Various types of analysis of the data can be automated using its machine-
processable form
• E.g., search or initiate some « action » for all instances of “associated morphology
is inflammation and consolidation”, which translates into “116676008=707496003”
that the machine then searches for in the database or initiates the « action » as
soon as any concept that has this attribute is found
NB: Additional processing steps may be required to perform all tasks, depending on the
system architecture
Dr SB Bhattacharyya© 11

12. THE ALGORITHM CONCEPTUALLY
• Accept a term from the user and process it as follows
• Find all its “Is a” relations from the relationships file
• Source is the term, type is “Is a”, destination is the supertype parent
• Find all its attribute (has a) relations from the relationships file
• The source is the term, type is the attribute name, destination is the attribute value
• NB:
• Relationship groupings are NOT taken into consideration
• Attributes may be repeated if the same attributes belong to different relationship groups
Dr SB Bhattacharyya© 12

13. THE ALGORITHM CONCEPTUALLY
• Join the Concept Ids using compositional grammar rules
• Rationale for not including the terms is that since the entire process is automated and
handled by the machine, not for human consideration, there is no justification in adding
additional complexities
• To visualise the various relationships, convert the Concept Ids into their
corresponding Terms
NB: Transitive closure table has been used
Dr SB Bhattacharyya© 13

14. “INTELLIGENT” INTERPRETATION
Automated Using SNOMED CT Description Logic
Dr SB Bhattacharyya© 14

15. HANDLING CLINICAL FINDING
Dr SB Bhattacharyya© 15
Entry Screen
Response Screen

16. HANDLING DISEASES
Dr SB Bhattacharyya© 16
Entry Screen
Response Screen

17. HANDLING INVESTIGATIONS
Dr SB Bhattacharyya© 17
Entry Screen
Response Screen

18. REFERENCES
• Completely designed and developed by Dr SB Bhattacharyya using material
found in
• SNOMED CT Technical Implementation Guide, IHTSDO
• SNOMED CT Compositional Grammar Specification and Guide, IHTSDO
• Introduction to SNOMED CT, SB Bhattacharyya
• Screenshot from the programme designed and developed by the author using
PHP and MySQL
Dr SB Bhattacharyya© 18

19. sbbhattacharyya@gmail.com
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20. Dr SB Bhattacharyya© 20
Thanks!