Clinical decision system for GIST cancer - Presentation

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Clinical decision support system for

GIST CancerAlessandro Seganti

Data Engineer

http://www.cognitum.eu/




Who we are : Cognitum

Fluent Editor

Ontorion Server

Ontorion Controlled Natural Language (OCNL)

Ask Your Data Anything (ADA)





IntroductionMedical background, approach and challenges





Medical background

GIST cancer

Little known, High error rate

Well specified recommendations

Manage the long clinical history of the patient

Clinical trial





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





Next

Application architecture

Knowledge base

Reasoning

Live presentation





Application architecture





Application Architecture





General idea

Domain knowledge

UI knowledge

UI

Generated knowledge





Knowledge base





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.





Histopathological report

Patient basic information

Surgery

Follow up

Medical Oncologist consultation

Systemic therapy start

Systemic therapy continue

Systemic therapy stop

Clinical trial





Automatic UI Generation





UI generation - Structure

FormSection

Field





UI generation - Fields

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





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.





Reasoning





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.





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.





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-1contains section-2.

SWRL statements.

Possible to use SWRL built-ins.





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.





Live demonstration





Live Demonstration

Cdss

Other example of applications using Semantic Forms



http://localhost/Cdss

http://localhost/PTChOBadanieMedyczne



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.





Thank you!Alessandro SegantiData Engineer

[email protected]

Cognitum Sp. z o.o.Wal Miedzeszynski 630, Warsaw, Poland

http://www.cognitum.eu/, [email protected]

Windows Azure Circle Partner

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Technology

Clinical decision system for GIST cancer - Presentation