Clinical decision system for GIST cancer - Presentation

1
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

2
Who we are : Cognitum
 Fluent Editor
 Ontorion Server
 Ontorion Controlled Natural Language (OCNL)
 Ask Your Data Anything (ADA)

3
Introduction
Medical background, approach and challenges

4
Medical background
 GIST cancer
 Little known, High error rate
 Well specified recommendations
 Manage the long clinical history of the patient
 Clinical trial

5
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

6
Next
 Application architecture
 Knowledge base
 Reasoning
 Live presentation

7
Application architecture

8
Application Architecture

9
General idea
Domain
knowledge
UI
knowledge
UI
Generated
knowledge

10
Knowledge base

11
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.

12
Histopathological report
Patient basic information
Surgery
Follow up
Medical Oncologist
consultation
Systemic therapy start
Systemic therapy continue
Systemic therapy stop
Clinical trial

13
Automatic UI Generation

14
UI generation - Structure
Form
Section
Field

15
UI generation - Fields
Every patient must have-date (some datetime value).

16
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.

17
Reasoning

18
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.

19
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.

20
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.

21
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.

22
Live demonstration

23
Live Demonstration
 Cdss
 Other example of applications using Semantic Forms

24
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.

25
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

Empfohlen

Empfohlen

Weitere ähnliche Inhalte

Kürzlich hochgeladen

Kürzlich hochgeladen (20)

Empfohlen

Empfohlen (20)

Clinical decision system for GIST cancer - Presentation