1. NeurOn: Modeling Ontology for Neurosurgery
K. S. Raghavan & C. Sajana
Indian Statistical Institute Bangalore

2. Information & Healthcare
Health care is a knowledge intensive activity; Available knowledge is a fluid mix of:
Scholarly documents
New experiences
Contextual information
Expert insights
collectively providing a framework for decision-making

3. Information & Healthcare
Patient records as an important source of valuable information
Much of this valuable information may not even appear in published sources or become a part of standard texts until years later
Quality of health care vis-à-vis access to patient records with defined similarities to the problem on hand

4. K. S. Raghavan, Indian Statistical Institute, Bangalore, India
Decision-making is… something which concerns all of us, both as makers of the choice and as sufferers of the consequences. -D. Lindley
K. S. Raghavan, Indian Statistical Institute, Bangalore, India

5. Background
The work reported here is set in a large hospital (and is in progress)
Present system & its limitations
WINISIS database with hyperlinks to related files (Such as X-rays, CT Scans, Pathology reports, etc)
Data on a large number of parameters
The range of relations between concepts in a complex domain such as health care
Search technologies in thesauri – based IR systems

6. The Present Study Hypotheses Why Ontology?
Ontologies can help build more effective information support systems in healthcare.
Ontologies can support the need of the healthcare and delivery process to transmit, re-use and share patient data
Why Ontology?
Ontologies are effective in representing domain knowledge
Possible to include ‘IF – THEN’ rules to support inferencing

7. Ontology?
Gruber’s Definition: “Explicit Specification of a Conceptualization”
Studer’s extension: “ a formal, explicit specification of a shared conceptualization”
For practical purposes and applications a domain ontology could be perceived as:
The complete set of domain concepts and their interrelationships

8. Ontology?
A semantic network of concepts grouped into classes and subclasses and linked by means of a well defined set of relations. Ontologies also have a set of rules that support inferencing

9. The Project
About 1500 patient records of the Neurosurgery unit of a large hospital
The neurological disorder
disease name (Final Diagnosis)
specific treatment
symptoms
associated illnesses
Patient Data; name, ID number, doctor’s name, disease index number, gender, age, age range, consciousness level, visual acuity details , etc

10. Neurological Disorder
The central theme of every patient record ran more or less like this:
A Patient
Neurological Disorder
that is
Has
Diagnosed
and
Treated
Method of Treatment
Using
Result
Leading to

11. Domain Concepts Patient Records contained four broad types of data:
These could be categorized under the 3 top level categories of Ranganathan, viz., Personality [P], Matter Property [MP] and Energy [E]
Medicine-Related Concepts
Patient-Related data
Healthcare Personnel Related data
Institution-Related data

13. Queries
In building the ontology it was important to have some idea of the nature of queries that the system should respond to:
Identify
Patients above 40 years of age and suffering from Astrocytoma
Patients with brain diseases having symptoms of headache and visual impairment
Records of patients who were administered drug XXX and had post – surgery complication of vision loss

14. Queries
While a clear picture will emerge only after the system is implemented;
A small query library was built to serve as the basis for defining classes and sub-classes

15. The Ontology No one perfect way of building an ontology
Definition of classes, properties, etc.
decisions regarding how detailed the classes and / or relations should be is largely based on the purpose and ease of maintenance
’female patients’ could be seen as a subclass of ‘patients’; it could also be handled as: Patient HAS GENDER and by fdefining acceptable values for gender

16. The Ontology
The decision-support system proposed here is conceived to have at least two major components when completed:
The ontology as part of the search interface; and
A database of patient records linked to related records in other databases, e.g. of images (scans, X-rays, etc)

17. Populating the Ontology
Phase 1
Medicine-related concepts - neurological disorders including associated symptoms and characteristics and their treatment –
patient data.
Other sub-domain concepts will be added later
Complex nature of relations

19. Populating the Ontology
Structuring the terms into a hierarchy
Inconsistencies in terminology used by hospital’s health-care personnel
SNOMED CT was used to standardize the terminology
Corresponding MeSH Terms & terms used by hospital personnel built in as relations:
[SNOMED CT for Domain Concept] <has physician’s term> [Term used in the patient record]

20. Populating the Ontology
Properties
Object
Data type
A general idea of the class hierarchy and properties (see Figure)

22. Future work To be implemented in the hospital
Only a limited number of patient records used
Tested with a few sample queries
To include concepts related to healthcare institutions and personnel
To link relevant manuals, reference sources, text books and papers with a view to widen the knowledge base available to the users of the decision support system

23. Future work To build rules for inferencing that allow
reasonable and intelligent guess of the probable cause or condition of a patient based on values of certain clinical parameters
decision regarding the possible course of action
defining parameters that could generate an alert message

24. Future work The complexity of medical terminology;
Non-availability of exact equivalents for some of the terms used (in patient records) in the standard medical terminologies (like MeSH and SNOMED CT)
Ethical issues
Can we define principles for decisions regarding ‘classes’ ‘subclasses’ ‘properties’ and ‘relation types’ for ontology?