Analyzing and integrating probabilistic and deterministic computational models in Synergy-COPD.
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.
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Analyzing and integrating probabilistic and deterministic computational models in Synergy-COPD.
- 1. Analyzing and integratingprobabilistic 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-COPDpresentation 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-COPDpresentation 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-COPDpresentation 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
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- 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
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- 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 supportsystem Luigi Ceccaroni and Filip Velickovski (Barcelona Digital Technology Centre, BDigital) EISBM workshop, Lyon, June 14, 2012
- 37. Clinical decision supportsystem 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’sCDSS 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) CDSSarchitecture 40
- 41. EISBM workshop (BDigital) Clinical-datarepresentation: 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-datarepresentation: vMR
- 43. EISBM workshop (BDigital) Clinical-datarepresentation: 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) Arule 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) CDSSreasoning paradigm 46
- 47. EISBM workshop (BDigital) Runningthe 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) Runningthe 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) Scopeof 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) Nextsteps 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) Clinicalsignificance 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) Clinicalsignificance 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) Clinicalsignificance 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) Patientdata 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 integratingprobabilistic 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)