Clinical decision system for GIST cancer - Presentation

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The company, product and service names used in this web site are for identification purposes only.
© Cognitum 2014. All trademarks and registered trademarks are the property of their respective owners.
Clinical decision
support system for
GIST Cancer
Alessandro Seganti
Data Engineer

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Who we are : Cognitum
 Fluent Editor
 Ontorion Server
 Ontorion Controlled Natural Language (OCNL)
 Ask Your Data Anything (ADA)

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Introduction
Medical background, approach and challenges

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Medical background
 GIST cancer
 Little known, High error rate
 Well specified recommendations
 Manage the long clinical history of the patient
 Clinical trial

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Requirements
 Improve efficiency of treatment and quality of the data
 No automatic decisions but recommendations
 Use standard recommendations depending on the
patient history
 Keep the patient clinical history so that it can be reused

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Next
 Application architecture
 Knowledge base
 Reasoning
 Live presentation

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Application architecture

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Application Architecture

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General idea
Domain
knowledge
UI
knowledge
UI
Generated
knowledge

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Knowledge base

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Knowledge Modeling
 How to model the long clinical history of a patient with
GIST cancer.
 Close collaboration with the domain expert
 OCNL to write the ontologies that can be understood by
the domain expert
 SWRL for Rules.
 Integrity Constraint rules for UI constraints.
 Annotations for flexibility.

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Histopathological report
Patient basic information
Surgery
Follow up
Medical Oncologist
consultation
Systemic therapy start
Systemic therapy continue
Systemic therapy stop
Clinical trial

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Automatic UI Generation

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UI generation - Structure
Form
Section
Field

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UI generation - Fields
Every patient must have-date (some datetime value).

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UI generation - Fields
Every patient must have-age (some integer value).
Every patient must have-sex a sex.
Something is a sex if-and-only-if-it is either Male or Female.

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Reasoning

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Challenges
 Reason efficiently and in a scalable way (independently
on the number of patients).
 Use knowledge dependent on time and reason over it.
 Show recommendations based on the patient history.

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Reasoning
 Ontorion Server to store the knowledge base in a
scalable way.
 Modularization based on unique patient namespace.
 Time dependent variables treated by having one
instance at each point of time.
 Only the latest instance is used for reasoning.
 Reasoning using OWL-RL+ profile.

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Reasoning: Transitions
 If some condition on form-1 then form-1 is followed by
another-form.
 If some condition on section-1 inside form-1 then form-1
contains section-2.
 SWRL statements.
 Possible to use SWRL built-ins.

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Recommendations
 Recommendations coming from the ESMO guidelines.
 Recommendations coming from good practices.
 Modelled using SWRL rules.
 Depending on the patient clinical history
 Another rule is used to decide in which form the
recommendation should be shown.

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Live demonstration

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Live Demonstration
 Cdss
 Other example of applications using Semantic Forms

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Conclusions
 Modeled medical knowledge base and applied it to build
a complex web application supporting physicians in the
daily work.
 Innovative approach allowing for scalable reasoning
independently on the number of patients.
 Currently used in the Oncological center in Warsaw
experimentally.
 Currently working to apply the same approach to other
type of cancer.

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Thank you!
Alessandro Seganti
Data Engineer
a.seganti@cognitum.eu
Cognitum Sp. z o.o.
Wal Miedzeszynski 630, Warsaw, Poland
http://www.cognitum.eu/, office@cognitum.eu
Windows Azure Circle Partner
MEMBER

Clinical decision system for GIST cancer - Presentation

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