NeurOn: Modeling Ontology for Neurosurgery

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NeurOn Modeling Ontology for Neurosurgery

• K. S. Raghavan C. Sajana
• Indian Statistical Institute Bangalore

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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

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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

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Decision-making is something which concerns all
of us, both as makers of the choice and as
sufferers of the consequences. -D. Lindley
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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

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The Present Study

• Hypotheses
• 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

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Ontology?

• Grubers Definition Explicit Specification of a
  Conceptualization
• Studers 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

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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

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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, doctors name,
  disease index number, gender, age, age range,
  consciousness level, visual acuity details , etc

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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
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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

Patient-Related data
Medicine-Related Concepts
Healthcare Personnel Related data
Institution-Related data
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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

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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

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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

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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)

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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

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Populating the Ontology

• Structuring the terms into a hierarchy
• Inconsistencies in terminology used by hospitals
  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 lthas physicians
  termgt Term used in the patient record

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Populating the Ontology

• Properties
• Object
• Data type
• A general idea of the class hierarchy and
  properties (see Figure)

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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

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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

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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?