The document discusses a clinical decision support system (CDSS) being developed as part of the Synergy-COPD project, which will use a Java-based framework and Drools rules engine to represent clinical knowledge and make inferences from patient data represented in a HL7 virtual medical record format, with the goal of aiding diagnosis and management of COPD patients.

1. Analyzing and integrating probabilistic and
deterministic computational models in Synergy-COPD
EISBM workshop, Lyon, June 14, 2012
Luigi Ceccaroni and Filip Velickovski
(Barcelona Digital Technology Centre, BDigital)
Isaac Cano (IDIBAPS)
David Gomez-Cabrero (Karolinska Institute)

2. EISBM workshop (BDigital)
Synergy-COPD presentation
Participants
• Barcelona Digital Technology Centre, BDigital (coordinator)
• Biomax Informatics AG
• Linkcare S.L.
• IDIBAPS
• Karolinska Institute
• The University of Oxford
• The University of Birmingham
• Infermed Ltd.
• Technical University of Budapest
2

3. EISBM workshop (BDigital)
Synergy-COPD presentation
Focus and budget
• Simulation environment and a decision-support system to
enable the deployment of systems medicine
• Core elements:
• knowledge base
• inference engine and simulation environment
• graphical visualization environments
• The proposal focuses on patients with chronic obstructive
pulmonary disease (COPD).
• Total budget: 5 M€
• Total EC contribution: 4 M€ 3

4. EISBM workshop (BDigital)
Synergy-COPD presentation
The point of view of the David’s
bio-researcher presentation
Isaac’s
presentation
4

5. Relationships between Oxygen
Transport/Utilization and
mitochondrial ROS generation:
computational prototype
Isaac Cano (IDIBAPS, Barcelona, Spain)
isaac.cano@linkcare.es
Isaac Cano
isaac.cano@linkcare.es
EISBM, Lyon 06/14/2012 6/18/2012 5
Linkcare Health Services SL

6. 1. Introduction

7. 2. c) Vertical model integration
Vertical integration (deterministic models)
CMISS Dr. Kelly Burrowes (UOXF.BL)
• Spatial heterogeneities of lung ventilation and perfusion.
FORTRAN Prof. Peter D. Wagner (UCSD)
Mr. Isaac Cano (HCPB)
• Central and peripheral O2 transport and utilization.
• Pulmonary gas exchange.
• Regional‐lung heterogeneities in ventilation and perfusion.
C++ Dr. Marta Cascante (UB)
Dr. Vitaly Selivanov (IDIBAPS, UB)
• Skeletal muscle bioenergetics.
• Mitochondrial ROS generation.
isaac.cano@linkcare.es 6/18/2012 7

8. 2. c) Vertical model integration
Vertical model integration (steady state conditions)
Oxygen transport and utilization ROS production
Normal
COPD
The higher the mitochondrial dysfunction, The lower the PmO2/P50 ratio,
the lower the PmO2/P50 ratio the higher the ROS production
6/18/2012 8

9. 2. c) Vertical model integration
Vertical model integration strategy
Oxygen transport
and utilization
Skeletal muscle bioenergetics &
Mitochondrial ROS generation
Relationships between
Oxygen
Transport/Utilization
and mitochondrial ROS
generation
isaac.cano@linkcare.es 6/18/2012 9

10. 3. Computational prototype
Vertical integration (Computational Prototype)
isaac.cano@linkcare.es 6/18/2012 10

11. 3. Computational prototype
Vertical integration (Computational Prototype)
isaac.cano@linkcare.es 6/18/2012 11

12. 3. Computational prototype
Vertical integration (Computational Prototype)
isaac.cano@linkcare.es 6/18/2012 12

13. 3. Computational prototype
Vertical integration (Computational Prototype)
isaac.cano@linkcare.es 6/18/2012 13

14. 3. Computational prototype
Vertical integration (Computational Prototype)
isaac.cano@linkcare.es 6/18/2012 14

15. 3. Computational prototype
Vertical integration (Computational Prototype)
isaac.cano@linkcare.es 6/18/2012 15

16. 3. Computational prototype
Vertical integration (Computational Prototype)
isaac.cano@linkcare.es 6/18/2012 16

17. 3. Computational prototype
Current work:
‐ Sensitivity analysis of the vertical integrated model.
‐ Extend the computational prototype to read/write model input parameters
encoded and stored in the Synergy‐COPD knowledge base.
‐ Direct connection with patient‐specific data from CT scan images.
‐ Extend the computational prototype to work as an API for the Synergy‐COPD
simulation environment.
isaac.cano@linkcare.es 6/18/2012 17

18. Questions?
isaac.cano@linkcare.es 6/18/2012 18

19. Probabilistic modeling
David Gomez-Cabrero (Karolinska Institute)
EISBM workshop, Lyon, June 14, 2012

20. EISBM Workshop.
Lyon, France, 14th June 2012
Mechanistic and Probabilistic
models…
All at once?
20

21. EISBM Workshop.
Lyon, France, 14th June 2012
Mechanistic and Probabilistic
models…
All at once?
Part 1: Predictive
networks
Part II: Integration
21

22. EISBM Workshop.
Lyon, France, 14th June 2012
Mechanistic and Probabilistic
models…
All at once?
Part 1: Predictive
networks
Part II: Integration
22

23. EISBM Workshop.
Lyon, France, 14th June 2012
Part I: predictive networks.
DATA
BIOBRIDGE
PAC-COPD
COPD at the time of a first hospital admission shows a wide variability
on its physio-pathological and clinical characteristics
Phenotypical heterogeneity in COPD can be classified in clinical /
epidemiologically relevant subtypes
These subtypes will differ on its clinical and functional course, use of
services and survival
Garcia-Aymerich J et al Thorax 2011 May;66(5):430-7

24. EISBM Workshop.
Lyon, France, 14th June 2012
Part I: predictive networks.
DATA
BIOBRIDGE
BHAM BME
Barabasi et al. Nature Reviews Genetics 12, 56-68 (2011)
NO QUANTITATIVE PREDICTION

25. EISBM Workshop.
Lyon, France, 14th June 2012
NETWORK
CONSTRUCTION
BME
Experimental/Predicti
Data Links Nodes
ve
Binary Interaction
Network (binary Experimental/(Binary
interactions from considered from Intact
Y2Hybrid, Intact and Mint database)
and Mint)
15315 6101
Binary Interaction
network Y2Hhybrid 7190 3302 Experimental/Binary
only
Transfac regulatory
1340 781 Experimental
network
Metabolic coupling
Experimental/
Interaction network 10642 921
Predictive
(BIGG/KEGG)
Curated HPRD-
74195 10890 Literature curated
Biogrid-Intact-Mint
CORUM-All
31276 2069 Experimental
complexes data
Kinase-substrate
327 (kinases)
pairs
6110 Experimental/Literature
(PhosphoSitePlus, 1771
Phospho.ELM) (substrates)
Barabási, New England Journal of Medicine (2007) 25

26. EISBM Workshop.
Lyon, France, 14th June 2012
NETWORK Extended genes,
CONSTRUCTION Topological Features
NETWORK
QUANTIFICATION
PREDICTIVE
STRATEGY:
BAYESIAN NETWORKS
- Small networks,
- Enough data?
- Predictive capacity?...
26

27. EISBM Workshop.
Lyon, France, 14th June 2012
STRATEGY:
BAYESIAN NETWORKS
Select regions of
- Small networks, interest
- Enough data?
- Predictive capacity?... Prior information
Validation by specific
BIOBRIDGE DATA
questions and PAC-
COPD
PRIOR INFORMATION
Public Resources
27

28. EISBM Workshop.
Lyon, France, 14th June 2012
The protein encoded by this gene is a
Core subunit of the Krebs tricarboxylic acid cycle enzyme
mitochondrial membrane that catalyzes the synthesis of citrate
Plays a role in intermediary respiratory chain NADH
metabolism and energy from oxaloacetate and acetyl coenzyme
dehydrogenase (Complex I)
A. The enzyme is found in nearly all cells
production. It may tightly that is believed
capable of oxidative metablism. This
associate or interact with the to belong to the minimal
assembly required for protein is nuclear encoded and
pyruvate dehydrogenase catalysis. transported into the mitochondrial matrix
complex where the mature form is found.
NDUFV1
IDH2 CS
Core subunit of the
mitochondrial
membrane respiratory
NDUFS2 chain NADH
VO2max
dehydrogenase
(Complex I) that is
believed
to belong to the
SDHA UQCRC1 minimal assembly
OGDH required for catalysis.
This is a component of the ubiquinol-
Flavoprotein (FP) subunit of succinate cytochrome c reductase complex
The 2-oxoglutarate dehydrogenase (complex III or cytochrome b-c1
dehydrogenase (SDH) that is involved in complex catalyzes the overall
complex II of the complex),
conversion of 2-oxoglutarate to which is part of the mitochondrial
mitochondrial electron transport chain succinyl-CoA
and is responsible for transferring respiratory chain. 28
and CO(2).
electrons from succinate to ubiquinone

29. EISBM Workshop.
Lyon, France, 14th June 2012
BAYESIAN NETWORKS: STRATEGY
NDUFV1
IDH2 CS
VO2max NDUFS2
SDHA UQCRC1 Not completely the same,
OGDH
Not necessary,
Input as resource
29

30. EISBM Workshop.
Lyon, France, 14th June 2012
BAYESIAN NETWORKS: STRATEGY
COPD TRAINING
How is training affecting our network? NDUFV1
IDH2 CS
How is COPD affecting our network?
Which genes do I need to affect in order to VO2max NDUFS2
increase VO2max in a COPD patient?
SDHA UQCRC1
OGDH
Is training enough for a COPD patient?
30

31. EISBM Workshop.
Lyon, France, 14th June 2012
Part I: predictive networks.
BAYESIAN NETWORKS: STRATEGY
DATA
Links clinical phenotypes, transcriptomics,…
BIOBRIDGE Pre-selected questions
But no trancriptomics here…
PAC-COPD We can use the models from BIOBRIDGE data?

32. EISBM Workshop.
Lyon, France, 14th June 2012
Mechanistic and Probabilistic
models…
All at once?
Part 1: Predictive
networks
Part II: Integration
32

33. EISBM Workshop.
Lyon, France, 14th June 2012
Deterministic models may not cover all the relevant aspects related to COPD.
IDENTIFY EXTEND
Qualitative networks allow to Bayesian Networks allow to generate
identify aspects not quantitative relations among data
considered in the model. that can cover those aspects and the
model,
33

34. EISBM Workshop.
Lyon, France, 14th June 2012
MECH MODEL GIVEN VALUES/PERTURBATIONS
PROVIDES NEW STEADY STATE
BAYESIAN NETWORK GIVEN VALUES/PERTURBATIONS
PROVIDES EXPECTATIONS/”UPDATE
IN BELIEF”
34

35. EISBM Workshop.
Lyon, France, 14th June 2012
SELECTING MODELS DEFINING OUTPUT/INPUT
BAYESIAN NETWORK GIVEN VALUES/PERTURBATIONS MECH MODEL GIVEN VALUES/PERTURBATIONS
PROVIDES NEW STEADY STATE
PROVIDES EXPECTATIONS/”UPDATE
IN BELIEF”
IDENTIFY LINKS
RUN MODEL A RUN MODEL B
35

36. Clinical decision support system
Luigi Ceccaroni and Filip Velickovski
(Barcelona Digital Technology Centre, BDigital)
EISBM workshop, Lyon, June 14, 2012

37. Clinical decision support system
Barcelona Digital Technology Centre, BDigital
EISBM workshop, Lyon, June 14, 2012

38. EISBM workshop (BDigital)
Topics
1. Synergy-COPD presentation
2. Summary of current status of CDSS in
Synergy-COPD
3. Clinical significance of models for COPD
patients
4. Patient data storage and retrieval
5. Scope of the CDSS
6. Use-case scenario
38

39. EISBM workshop (BDigital)
Synergy-COPD’s CDSS features
• Framework: Java-based
• Decision engine: Drools rules engine
• Clinical-knowledge representation: Drool rules
for:
a. Case-finding / screening
b. Confirming COPD diagnosis
c. Initial disease assessment (based on the stages of
GOLD)
• EHR’s API: HL7 virtual medical record (vMR, XML
format) (imported via JAXB interface)
39

40. EISBM workshop (BDigital)
CDSS architecture
40

41. EISBM workshop (BDigital)
Clinical-data representation: vMR
• Patient data in the CDSS are represented as a HL7 virtual
medical record (vMR) XML format.
• vMR is an evolving HL7 information model for representing
personal data relevant to clinical decision support in a
formal format.
• vMR enables the use of standardized nomenclatures for
data inputs and outputs.
• vMR is aligned with preexisting HL7 data models (RIM,
CCD), with a structure especially suited for clinical inference.
41

42. EISBM workshop (BDigital)
Clinical-data representation: vMR

43. EISBM workshop (BDigital)
Clinical-data representation: vMR

44. EISBM workshop (BDigital)
Data representation: vMR example
<!-- Clinical statement for dyspnea symptom -->
<clinicalStatement xsi:type="ObservationResult">
<!-- Unique ID of the clinical statement -->
<id root="d0f9e07c-4867-4202-a1b9-bb1bb8946008"/>
<observationEventTime>
<low value="20101122"/> <high value="20120129"/>
</observationEventTime>
<observationValue xsi:type="CD" code="391125004"
codeSystem="2.16.840.1.113883.6.96"
codeSystemName="SNOMED-CT">
<displayName value="grade 4"/>
</observationValue>
</clinicalStatement>
44

45. EISBM workshop (BDigital)
A rule from case finding
rule "COPD symptoms - Dyspnea"
when
$state : SystemState(context == Context.SCREENING)
VMR( $patient : patient )
# Dyspnea code is 267036007
$obsr : ObservationResult(observationFocus.code ==
“267036007”, observationFocus.codeSystem ==
“2.16.840.1.113883.6.96”)
then
logger.info("Patient has dyspnea");
insert(new Tag($obsr, "COPD symptom"));
end
45

46. EISBM workshop (BDigital)
CDSS reasoning paradigm
46

47. EISBM workshop (BDigital)
Running the CDSS
INFO: Initiating Reasoning Engine...
30-ene-2012 16:23:18 cdss.re.ReasoningEngine <init>
cdss.re.Rule_spirometry_measurement_imples_pulmonary_obstruction
INFO: spirometry measurement implies pulmonary obstruction
cdss.re.Rule_spirometry_measurement_imples_pulmonary_obstruction
INFO: FEV1 is :2.0 FVC is :3.6
30-ene-2012 16:23:22
cdss.re.Rule_spirometry_measurement_is_recent_0
INFO: Measurement is recent
30-ene-2012 16:23:22 cdss.re.Rule_confirmation_of_COPD_0
defaultConsequence
INFO: Recommendation: Diagnose patient as COPD.
30-ene-2012 16:23:22 cdss.re.Rule_Derive_age_from_birthtime_0
defaultConsequence
INFO: Age 60.0 added
47

48. EISBM workshop (BDigital)
Running the CDSS
Output
Recommendation: Diagnose patient as COPD.
Reason: The most recent spirometry result for
this COPD candidate has met the criteria:
1. Measurement is recent and of high quality.
2. Spirometry is performed after bronchodilation.
3. Measurement implies pulmonary obstruction
(FEV1 / FVC < 0.7).
48

49. EISBM workshop (BDigital)
Scope of CDSS
Current the Synergy-COPD CDSS engine does:
• Simple screening based on symptoms
• Diagnosis based on GOLD guideline criteria using:
• Symptoms
• Spirometry measurements
• Assessment:
• COPD Severity based on GOLD guideline
criteria.
49

50. EISBM workshop (BDigital)
Next steps in Synergy-COPD CDSS
• To expand clinical scope:
• To make recommendations about
prognosis and treatment.
• To develop a graphical visualization
environment:
• GUI for the clinicians
• Human readable rules:
• To map a domain-specific language to
drool rules.
50

51. EISBM workshop (BDigital)
Clinical significance of models
• Which are the "outputs" of the models that can be used in the
decision support of a clinical task?
• Suggested areas:
• Screening:
• Early prediction
51

52. EISBM workshop (BDigital)
Clinical significance of models
• Which are the "outputs" of the models that can be used in the
decision support of a clinical task?
• Suggested areas:
• Assessment:
• Better way of assessing COPD:
• Via phenotyping
• Via new severity indicator (better than GOLD, FEV1,
BODE)
• Future health status prediction based on current state
• Which next tests to do given the current health state of the
COPD patient and when?
52

53. EISBM workshop (BDigital)
Clinical significance of models
• Which are the "outputs" of the models that can be used in the
decision support of a clinical task?
• Suggested areas:
• Management (treatment):
• Predicting the outcomes of different treatment regimes for
specific patient profiles:
• Long acting Beta2-Agonists
• vs. Short acting Beta2-Agonists
• vs. Corticosteroids
• vs. Combinations
• Dosage calculator
• Best therapy recommender 53

54. EISBM workshop (BDigital)
Patient data storage and retrieval
• Currently the “EHR” is simulated as HL7
XML files in the CDSS.
• In a real system of this kind where are the
patient's personal clinical data that the
CDSS needs stored?
• Need to integrate our knowledge bases with
healthcare institutions’ MHRs
54

55. Analyzing and integrating probabilistic and
deterministic computational models in Synergy-COPD
EISBM workshop, Lyon, June 14, 2012
Luigi Ceccaroni and Filip Velickovski
(Barcelona Digital Technology Centre, BDigital)
Isaac Cano (IDIBAPS)
David Gomez-Cabrero (Karolinska Institute)