Presentation made by Alessandro Seganti during Semantic2015 Conference in Vienna. Clinical decision support system application are a class of application to support physicians in their choice in treating patients. Cancer treatment is one of the area where CDSS applications can help physicians due to strict recommendations and the need for deciding if patients are eligible to enter clinical trial. To learn more, visit our website: http://www.cognitum.eu/

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© 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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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.
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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© Cognitum 2014. All trademarks and registered trademarks are the property of their respective owners.
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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© Cognitum 2014. All trademarks and registered trademarks are the property of their respective owners.
Next
 Application architecture
 Knowledge base
 Reasoning
 Live presentation

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© Cognitum 2014. All trademarks and registered trademarks are the property of their respective owners.
Application architecture

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© Cognitum 2014. All trademarks and registered trademarks are the property of their respective owners.
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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© Cognitum 2014. All trademarks and registered trademarks are the property of their respective owners.
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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© Cognitum 2014. All trademarks and registered trademarks are the property of their respective owners.
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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© Cognitum 2014. All trademarks and registered trademarks are the property of their respective owners.
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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© Cognitum 2014. All trademarks and registered trademarks are the property of their respective owners.
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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© Cognitum 2014. All trademarks and registered trademarks are the property of their respective owners.
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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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.
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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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.
Live demonstration

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

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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.
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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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.
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
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