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1 Eric Neumann Clinical Semantic Group W3C HCLS chair, MIT Fellow Tutorial: Semantic Web Applications in Clinical Data Management.

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Presentation on theme: "1 Eric Neumann Clinical Semantic Group W3C HCLS chair, MIT Fellow Tutorial: Semantic Web Applications in Clinical Data Management."— Presentation transcript:

1 1 Eric Neumann Clinical Semantic Group W3C HCLS chair, MIT Fellow Tutorial: Semantic Web Applications in Clinical Data Management

2 2 Tutorial Overview Bench-to-Bedside VisionBench-to-Bedside Vision Information ChallengesInformation Challenges Semantic Web: What is it?Semantic Web: What is it? –RDF: Recombinant Data (Aggregation) –OWL: Vocabularies (NCI, SNOMED) –Rules Translational Medicine NeedsTranslational Medicine Needs Clinical Data Standards- CDISCClinical Data Standards- CDISC Re-Using Clinical KnowledgeRe-Using Clinical Knowledge –Retrospective DBs: JANUS –Open Knowledge Benefits: Tox Commons

3 3 Bench-to-Bedside Connecting pre-clinical and clinical studiesConnecting pre-clinical and clinical studies –Translational Medicine Patient Stratification & Personalized medicine (not the same)Patient Stratification & Personalized medicine (not the same) Knowledge and Data IntegrationKnowledge and Data Integration –Better Disease Understanding –Next Generation Therapies, New Applications More Predictive (earlier) Safety SignalsMore Predictive (earlier) Safety Signals

4 4 from Innovation or Stagnation, FDA Report March 2004

5 5 New Regulatory Issues Confronting Pharmaceuticals from Innovation or Stagnation, FDA Report March 2004 Tox/Efficacy ADME Optim

6 6 Translational Medicine Enable physicians to more effectively translate relevant findings and hypotheses into therapies for human healthEnable physicians to more effectively translate relevant findings and hypotheses into therapies for human health Support the blending of huge volumes of clinical research and phenotypic data with genomic research dataSupport the blending of huge volumes of clinical research and phenotypic data with genomic research data Apply that knowledge to patients and finally make individualized, preventative medicine a reality for diseases that have a genetic basisApply that knowledge to patients and finally make individualized, preventative medicine a reality for diseases that have a genetic basis

7 7 Drug Discovery & Development Knowledge Qualified Targets Lead Generation Toxicity & Safety Biomarkers Pharmacogenomics Clinical Trials Molecular Mechanisms Lead Optimization Launch

8 8 Ecosystem: Goal State Merging Biomed Research, Clinical Trials and Clinical Practice Biomedical ResearchClinical Practice

9 9 Drug R&D HCP Biomed Research Insurers Gov/Regulatory Public CROs Gov/Funding Grants Publications and Public Databases Disease Areas Chem Manuf Mol Path ResClin Res BiomarkerT ox Preclin Clin Safety Clin POCSurveilla nce Drug Programs Large Studies HMO,PPO Marketing VA System R&D BKB Safety Commons Risks & Benefits JANUS HC Choices EHR HCLS Ecosystem

10 10 Information Challenges No common way to bring data and docs togetherNo common way to bring data and docs together –HTML links carries no meaning with them Todays integration approaches prevent data re-useTodays integration approaches prevent data re-use No global way to annotate our experiments and experiencesNo global way to annotate our experiments and experiences –Most annotations cannot be found by context –No sci-blog for data interpretation Enterprise Information access and discoverability are weakEnterprise Information access and discoverability are weak –Making timely discoveries! –Why we all like Google Cutting and pasting between docs promotes fact mutation and loss of provenanceCutting and pasting between docs promotes fact mutation and loss of provenance –Address business operations and tracking, and reduce static data copying

11 11 A web of information Courtesy of R. Stevens

12 12 Diseases Assays Distributed Nature of Biomedical Knowledge Silos of Data… Clinical Trials Targets Tox Patents Libraries Biomarkers Genotypes Drug Registry HCS

13 13 The Big Picture In Drug R&D Hard to understand from just a few isolated Points of View

14 14 What if Scientists could put it together for themselves?

15 15 Complete view tells a very different Story

16 16 Whose Schema? DiseaseSubjectsGenotypeClinical Papers Enrollment Criteria Audit Trail Tox Signals Statistics Ontology Trials Dosing Observations

17 17 Why Searching ala Google is not enough Googles ability to rank and graph without using semantics is comparable to… … a Drug R&D Project that looks for associations, but makes no attempt to find or represent mechanisms of action … a Drug R&D Project that looks for associations, but makes no attempt to find or represent mechanisms of action

18 What is the Semantic Web?

19 19 The Layer Cake

20 20 The Current Web What the computer sees: Dumb links What the computer sees: Dumb links No semantics - treated just like No semantics - treated just like Minimal machine- processable information Minimal machine- processable information

21 21 The Semantic Web Machine-processable semantic information Machine-processable semantic information Semantic context published – making the data more informative to both humans and machines Semantic context published – making the data more informative to both humans and machines

22 22 Needed to realize the SW vision A standard way of identifying thingsA standard way of identifying things A standard way of describing thingsA standard way of describing things A standard way of linking thingsA standard way of linking things Standard vocabularies for talking about thingsStandard vocabularies for talking about things

23 23 The Semantic Web Basic Standards for Describing Things Richer structure for basic resources (XML) Richer structure for basic resources (XML) Describe Data by Semantics and Not Syntax: RDF Describe Data by Semantics and Not Syntax: RDF Define Semantics using RDFS or OWL Define Semantics using RDFS or OWL Reference and Relate All Resources using URIs Reference and Relate All Resources using URIs SPARQL is super model of SQL SPARQL is super model of SQL Rules for higher level reasoning Rules for higher level reasoning

24 24 The Technologies: RDF Resource Description Framework (RDF)Resource Description Framework (RDF) W3C standard for making statements or hypotheses about data and conceptsW3C standard for making statements or hypotheses about data and concepts Descriptive statements are expressed as triples: (Subject, Verb, Object)Descriptive statements are expressed as triples: (Subject, Verb, Object) SubjectObject Property

25 25 Facts as triples PARK1 Parkinson disease has_associated_disease subjectpredicateobject

26 26 From triples to a graph PARK1Parkinson disease has_associated_disease MAPTParkinson disease MAPTPick disease TBPParkinson disease TBPSpinocerebellar ataxia PARK1Parkinson disease MAPT Pick disease Parkinson disease TBP Spinocerebellar ataxia PARK1Parkinson disease MAPTPick disease TBPSpinocerebellar ataxia

27 27 Connecting graphs Integrate graphs from multiple resourcesIntegrate graphs from multiple resources Query across resourcesQuery across resources APPAlzheimer disease PARK1Parkinson disease has_associated_disease Alzheimer disease Parkinson disease Neurodegenerative diseases isa

28 28 Semantic Web Technologies Richer structure for resourcesRicher structure for resources –eXtensible Markup Language (XML) Exposed semanticsExposed semantics –Resource Description Framework (RDF) Explicit semanticsExplicit semantics –Ontologies –Web Ontology Language (OWL)

29 29 The URI - global identification URI serves as a universal and uniform identifier for all web based resources.

30 30 A Family of Identifiers URI = Uniform Resource Identifier URL = Uniform Resource Locator URN = Uniform Resource Name LSID = Life Science Identifier URI URLURN LSID URI= Uniform Resource Identifier URL= Uniform Resource Locator URN= Uniform Resource Name LSID= Life Science Identifier http://www.w3.org/Addressing/

31 31 Uniform Resource Locator A type or resource identifierA type or resource identifier Identifies the location of a resource (or part thereof)Identifies the location of a resource (or part thereof) Specifies a protocol to access the resourceSpecifies a protocol to access the resource –http, ftp, mailto E.g.,E.g., –http://www.nlm.nih.gov/ URI URL URN LSID

32 32 Uniform Resource Name A type or resource identifierA type or resource identifier Identifies the name of a resourceIdentifies the name of a resource Location independentLocation independent Defines a namespaceDefines a namespace E.g.,E.g., –urn:isbn:0-262-02591-4 –urn:umls:C0001403 URI URL URN LSID

33 33 Life Science Identifier A type or resource identifierA type or resource identifier A type of URNA type of URN For biological entitiesFor biological entities Specific propertiesSpecific properties –Versioned –Resolvable –Immutable E.g.,E.g., URI URLURN LSID http://lsid.sourceforge.net/ urn:lsid:ncbi.nlm.nih.gov:pubmed:12571434 DNS name namespace unique ID

34 34 RDF Examples …as RDF-XML …as N3 a cdisc:Subject ; a cdisc:Subject ; nci:sex_code nci:Female ; nci:sex_code nci:Female ; cdisc:treatment ; cdisc:vitalSigns ; cdisc:adverseEvent. cdisc:treatment ; cdisc:vitalSigns ; cdisc:adverseEvent.

35 35 Semantic Data Integration: Incremental Roadmap Data assets remain as they are! They do not need to be modifiedData assets remain as they are! They do not need to be modified The wrapper abstracts out details related to location, access and data structureThe wrapper abstracts out details related to location, access and data structure Integration happens at the information levelIntegration happens at the information level Highly configurable and incremental processHighly configurable and incremental process Ability to specify declarative rules and mappings for further hypothesis generationAbility to specify declarative rules and mappings for further hypothesis generation

36 36 RDBM => RDF Virtualized RDF {primary keys}

37 37 Semantic Data Integration Bridging Clinical and Genomic Information Paternal 1 type degree Patient (id = URI1) Mr. X name Person (id = URI2) related_to FamilyHistory (id = URI3) has_family_history Sudden Death problem associated_relative EMR Data Patient (id = URI1) MolecularDiagnosticTestResult (id = URI4) has_structured_test_result MYH7 missense Ser532Pro (id = URI5) identifies_mutation Dialated Cardiomyopathy (id = URI6) indicates_disease LIMS Data Rule/Semantics-based Integration: - Match Nodes with same Ids - Create new links: IF a patients structured test result indicates a disease THEN add a suffers from link to that disease 90% evidence1 95% evidence2

38 38 Semantic Data Integration: Bridging Clinical and Genomic Information RDF Graphs provide a semantics-rich substrate for decision support. Can be exploited by SWRL Rules Patient (id = URI1) Mr. X name Person (id = URI2) related_to FamilyHistory (id = URI3) has_family_history Sudden Death problem Paternal 1 type degree associated_relative StructuredTestResult (id = URI4) MYH7 missense Ser532Pro (id = URI5) identifies_mutation Dialated Cardiomyopathy (id = URI6) indicates_disease has_structured_test_result suffers_from has_gene 90% evidence

39 39 Drug Discovery Dashboard http://www.w3.org/2005/04/swls/BioDash Topic: GSK3beta Topic Target: GSK3beta Disease: DiabetesT2 Alt Dis: Alzheimers Cmpd: SB44121 CE: DBP Team: GSK3 Team Person: John Related Set Path: WNT Semantic Data Integration and Visualization: Drug Discovery

40 40 Semantic Data Integration: Bridging Chemistry and Molecular Biology urn:lsid:uniprot.org:uniprot: P49841 Semantic Lenses: Different Views of the same data Apply Correspondence Rule: if ?target.xref.lsid == ?bpx:prot.xref.lsid then ?target.correspondsTo.?bpx:prot BioPax Components Target Model

41 41 Lenses can aggregate, accentuate, or even analyze new result sets Behind the lens, the data can be persistently stored as RDF-OWL Correspondence does not need to mean same descriptive object, but may mean objects with identical references Semantic Data Integration Bridging Chemistry and Molecular Biology

42 42 Semantic Data Integration Pathway Polymorphisms Merge directly onto pathway graph Identify targets with lowest chance of genetic variance Predict parts of pathways with highest functional variability Map genetic influence to potential pathway elements Select mechanisms of action that are minimally impacted by polymorphisms Non-synonymous polymorphisms from db-SNP

43 43 Scenario: Biomarker Qualification Semantics which Define…Semantics which Define… Biomarker RolesBiomarker Roles –Disease –Toxicity –Efficacy Molecular and cytological markersMolecular and cytological markers –Tissue-specific –High content screening derived information –Different sets associated with different predictive tools Statistical discrimination based on selected samplesStatistical discrimination based on selected samples –Predictive power –Alternative cluster prediction algorithms –Support qualifications from multiple studies (comparisons) Causal mechanismsCausal mechanisms –Pathways –Population variation

44 44 Semantic Data Integration: Advantages RDF: Graph based data modelRDF: Graph based data model –More expressive than the tree based XML Schema Model RDF: ReificationRDF: Reification –Same piece of information can be given different values of belief by different clinical genomic researchers Potential for Schema-less Data IntegrationPotential for Schema-less Data Integration –Hypothesis driven approach to defining mapping rules –Can define mapping rules on the fly Incremental approach for Data IntegrationIncremental approach for Data Integration –Ability to introduce new data sources into the mix incrementally at low cost Use of Ontology to disallow meaningless mapping rules?Use of Ontology to disallow meaningless mapping rules? –For e.g., mapping a gene to a protein…

45 45 Semantic Data Integration Schema-free data integration Low cost approach for data integrationLow cost approach for data integration No need for maintenance of costly schema mappingsNo need for maintenance of costly schema mappings Ability to merge RDF graphs based on simple declarative rules that specify:Ability to merge RDF graphs based on simple declarative rules that specify: –Equality of URIs –Connecting nodes of same type –Connecting two nodes associated by a path Disadvantage: Potential for specifying spurious non-sensical rulesDisadvantage: Potential for specifying spurious non-sensical rules

46 46 Semantic Data Integration Use of Reification Level of accuracy of test result.Level of accuracy of test result. –Sensitivity and Specificity of lab result –Level of confidence in genotyping or gene sequencing Probabilistic relationshipsProbabilistic relationships –Likelihood that a particular test result or condition is indicative of a disease or other medical condition Level of trust in a resourceLevel of trust in a resource –Results from a lab may be trusted more than result from another –Results from well known health sites (NLM) may be trusted more than others Belief attributionBelief attribution –Scientific hypotheses may be attributed to appropriate researchers

47 47 The Available Data Space Separate RDF documents are merged automatically into one aggregate graph.

48 48 Recombination in Molecular Genetics works due to proper alignment of genetic regions, thereby preventing gene loss, mangling, or duplication.

49 49 Recombinant Data Graphs can be filtered and pivoted, without losing meaning

50 50 Recombinant Data Mash-ups that dont lose perspectiveMash-ups that dont lose perspective Dynamic mixing of dataDynamic mixing of data Provide Different Views for Different Roles and FunctionsProvide Different Views for Different Roles and Functions –Dashboards Direct output of a SPARQL queryDirect output of a SPARQL query

51 51 Key Functionality offered by Semantic Web UbiquityUbiquity –Same identifiers for anything from anywhere DiscoverabilityDiscoverability –Global search on any entity InteroperabilityInteroperability –=> Recombinant Data is Application Independence

52 52 Data Vision Aggregating data and statements using the WebAggregating data and statements using the Web –Defined aggregation by need and role –Recombinant Data Common system of referencing things (no copying)Common system of referencing things (no copying) –even is they exits in one of many databases Indexing things by types and with tagsIndexing things by types and with tags –Common and ad hoc vocabularies Supporting the collective knowledge of an R&D CommunitySupporting the collective knowledge of an R&D Community –A Wiki that has awareness about types and things –New Generation Discovery Tools

53 53 Ontologies and Web Ontology Language (OWL)

54 54 OWL Introduction History: DAML + OIL = OWL(2001)History: DAML + OIL = OWL(2001) –DAML – DARPA Agent Markup Language(1999) –OIL – Ontology Inference Layer(1997) Based on RDF(S)Based on RDF(S) Added features, mostly related to identityAdded features, mostly related to identity –Restrictions Three flavors of increasing expressiveness, but decreasing tractabilityThree flavors of increasing expressiveness, but decreasing tractability –OWL Lite –OWL DL (used for most applications) –OWL Full

55 55 Ontology Dimensions based on McGuinness and Finin Simple Terminologies Expressive Ontologies Catalog General Logical constraints Terms/ glossary Thesauri: BT/NT, Parent/Child, Informal Is-A Formal is-a Frames (Properties) Formal instances Value Restriction Disjointness, Inverse MeSH, Gene Ontology, UMLS Meta CYC RDF(S) DB Schema IEEE SUOOWL KEGG TAMBIS EcoCyc BioPAX Ontylog Snomed Medication Lists DDI Lists The Knowledge Semantics Continuum

56 56 OWL DL Example Class: Benign intracranial meningioma in the NCI ThesaurusClass: Benign intracranial meningioma in the NCI Thesaurus Benign Intracranial Meningioma C5133 Benign Intracranial Meningioma Neoplastic Process Benign Intracranial Meningioma […] CL006955 Benign Intracranial Meningioma C5133 Benign Intracranial Meningioma Neoplastic Process Benign Intracranial Meningioma […] CL006955 http://cancer.gov/cancerinfo/terminologyresources/

57 57 OWL Class Constructors Borrowed from Tutorial on OWL by Bechhofer, Horrocks and Patel-Schneider http://www.cs.man.ac.uk/~horrocks/ISWC2003/Tutorial/

58 58 OWL Axioms Axioms (mostly) reducible to inclusion (v)Axioms (mostly) reducible to inclusion (v) – C ´ D iff both C v D and D v C Borrowed from Tutorial on OWL by Bechhofer, Horrocks and Patel-Schneider http://www.cs.man.ac.uk/~horrocks/ISWC2003/Tutorial/

59 59 Existential vs. Universal Quantification Existential quantificationExistential quantification –owl:someValuesFrom –Necessary condition –E.g., migraine = headache & has_symptom throbbing pain [only if one-sided] Universal quantificationUniversal quantification –owl:allValuesFrom –Necessary and sufficient condition –E.g., heart disease = disease & located_to heart

60 60 OWL reasoners For OWL DL, not OWL FullFor OWL DL, not OWL Full ReasonersReasoners –Fact++ –Pellet –RacerPro FunctionsFunctions –Consistency checking –Automatic classification http://www.mindswap.org/2003/pellet/ http://www.racer-systems.com/ http://owl.man.ac.uk/factplusplus/

61 61 OWL Reasoners Details CELCEL –Polynomial time classifier for the description logic EL+ –EL+ is specially geared towards biomedical ontologies CerebraCerebra –Commerical C++ reasoner, Support for OWL-API –Tableaux based reasoning for TBoxes and ABoxes Fact++Fact++ –Free open source reasoner for DL reasoning –Support for Lisp API and OWL API KAON2KAON2 –Free Java based DL reasoner with support for SWRL fragment –Support for DIG API MSPASSMSPASS –A generalized theorem prover for numerous logics, also works for DLs PelletPellet –Free open source Java based reasoner for DLs –Support for OWL, DIG APIs and Jena Interface RacerProRacerPro –Commercial lisp based reasoner for DLs –Support for OWL APIs and DIG APIs

62 62 Editing OWL ontologies http://protege.stanford.edu/

63 63 Resources available in OWL Many resources currently available in OWLMany resources currently available in OWL –Gene Ontology –NCI Thesaurus Many projects using OWLMany projects using OWL –e.g., BioPax NCBO - Mark Musen, DirectorNCBO - Mark Musen, Director http://www.geneontology.org/ http://cancer.gov/cancerinfo/terminologyresources/ http://www.biopax.org/

64 64 OBO format Used to represent many ontologies in the OBO family (Open Biological Ontologies)Used to represent many ontologies in the OBO family (Open Biological Ontologies) Essentially a subset of OWL DLEssentially a subset of OWL DL http://obo.sourceforge.net/ [Term] id: GO:0019563 name: glycerol catabolism namespace: biological_process def: "The chemical reactions and pathways resulting in the breakdown of glycerol … subset: gosubset_prok exact_synonym: "glycerol breakdown" [] exact_synonym: "glycerol degradation" [] xref_analog: MetaCyc:PWY0-381 is_a: GO:0006071 ! glycerol metabolism is_a: GO:0046174 ! polyol catabolism [Term] id: GO:0019563 name: glycerol catabolism namespace: biological_process def: "The chemical reactions and pathways resulting in the breakdown of glycerol … subset: gosubset_prok exact_synonym: "glycerol breakdown" [] exact_synonym: "glycerol degradation" [] xref_analog: MetaCyc:PWY0-381 is_a: GO:0006071 ! glycerol metabolism is_a: GO:0046174 ! polyol catabolism http://www.godatabase.org/dev/doc/obo_format_spec.html

65 65 Domain Semantics in Clinical Trials Clinical Semantics Patient/Subject Disease/Health statePatient/Subject Disease/Health state Diagnostics FindingsDiagnostics Findings Findings Inferred (proposed) Disease stateFindings Inferred (proposed) Disease state Disease state Patient Classification / SegmentationDisease state Patient Classification / Segmentation Design Trial arms / treatmentsDesign Trial arms / treatments Observation POC, safety, mechanismsObservation POC, safety, mechanisms

66 66 Linking Clinical Ontologies with the Semantic Web Clinical Trials ontology RCRIM (HL7) Genomics CDISC IRB Applications Molecules Clinical Obs ICD10 Pathways (BioPAX) Disease Models Extant ontologies Mechanisms Under development Bridge concept SNOMED Disease Descriptions Tox

67 67 Ontology Referencing { "@context": "http://schema.org", "@type": "ImageObject", "contentUrl": "http://images.slideplayer.com/2/725865/slides/slide_67.jpg", "name": "67 Ontology Referencing

68 68 Rules and Policies

69 69 Renal disease =Renal disease = –Chronic Renal Failure Nephropathy, chronic renal failure, end-stage renal disease, renal insufficiency, hemodialysis, peritoneal dialysis on Problem List (SNOMED)Nephropathy, chronic renal failure, end-stage renal disease, renal insufficiency, hemodialysis, peritoneal dialysis on Problem List (SNOMED) Creatinine > 2Creatinine > 2 Calculated GFR < 50Calculated GFR < 50 –Malb/creat ratio test > 30 DiabetesDiabetes Many variants on the problem listMany variants on the problem list On Insulin or oral hypoglycemic drugOn Insulin or oral hypoglycemic drug Contraindication to ACE inhibitorContraindication to ACE inhibitor Allergy, Cough on ACE on adverse reaction list, or Hyperkalemia on problem list, Pregnant (20 sub rules to define this state)Allergy, Cough on ACE on adverse reaction list, or Hyperkalemia on problem list, Pregnant (20 sub rules to define this state) K test result > 5K test result > 5 Imagine this CDS Rule: If Renal Disease and DM and no contraindication, should be on ACE inhibitor or ARB

70 70 Translational Medicine

71 71 Translational Medicine in Drug R&D In Vitro StudiesAnimal StudiesClinical Studies Toxicities Target/System Efficacy Early Middle Late Cellular Systems Human Disease Models (Therapeutic Relevance) $ $$ $$$

72 72 Case Study: Drug SafetySafety Lenses Lenses can focus data in specific waysLenses can focus data in specific ways – Hepatoxicity, genotoxicity, hERG, metabolites Can be wrapped around statistical toolsCan be wrapped around statistical tools Aggregate other papers and findings (knowledge) in context with a particular projectAggregate other papers and findings (knowledge) in context with a particular project Align animal studies with clinical resultsAlign animal studies with clinical results Support special Alert-channels by regulators for each different toxicity issueSupport special Alert-channels by regulators for each different toxicity issue Integrate JIT information on newly published mechanisms of actionsIntegrate JIT information on newly published mechanisms of actions

73 73 ClinDash: Clinical Trials Browser Clinical Obs Expression Data Subjects Values can be normalized across all measurables (rows) Samples can be aligned to their subjects using RDF rules Clustering can now be done over all measureables (rows)

74 74 GeneLogic GeneExpress Data Additional relations and aspects can be defined additionally Diseased Tissue Links to OMIM (RDF)

75 75 ClinDash: Clinical Trials Browser Clinical Obs Expression Data Subjects Values can be normalized across all measurables (rows) Samples can be aligned to their subjects using RDF rules Clustering can now be done over all measureables (rows)

76 76 EDC and EHR Should they be merged?Should they be merged? –Differences in goals and implementations Reduce data redundancyReduce data redundancy The Semantic Web solutionThe Semantic Web solution –Use EHR RDF to generate part of EDC frame –Use same URIs for patient, clinic entities

77 77 AE Channels Female service members reported higher rates of reactions to the previous dose of vaccine during anthrax vaccination of all U.S. military personnel. Female service members reported higher rates of reactions to the previous dose of vaccine during anthrax vaccination of all U.S. military personnel. http://www.cdc.gov/MMWR/48e905bdb66310af85ad2e8503628e01 Posted by alan_zimmers to health.mil/adverse_events&Processes on Thu Jan 19 2006 Posted by alan_zimmers to health.mil/adverse_events&Processes on Thu Jan 19 2006 alan_zimmers alan_zimmers 2006-01-19T11:24:03ZAdverseEvent Anthrax Vaccination&Treatment Anthrax Vaccination&Treatment Female service members reported higher rates of reactions to the previous dose of vaccine during anthrax vaccination of all U.S. military personnel. Female service members reported higher rates of reactions to the previous dose of vaccine during anthrax vaccination of all U.S. military personnel. A Sainz-Perez A Sainz-Perez H Gary-Gouy H Gary-Gouy 16408101 16408101 PMID: 16408101 PMID: 16408101 2006-01-12 2006-01-12 Leukemia Leukemia 0887-6924 0887-6924

78 78 AE Channels Female service members reported higher rates of reactions to the previous dose of vaccine during anthrax vaccination of all U.S. military personnel. Female service members reported higher rates of reactions to the previous dose of vaccine during anthrax vaccination of all U.S. military personnel. http://www.cdc.gov/MMWR/48e905bdb66310af85ad2e8503628e01 Posted by alan_zimmers to health.mil/adverse_events&Processes on Thu Jan 19 2006 Posted by alan_zimmers to health.mil/adverse_events&Processes on Thu Jan 19 2006 alan_zimmers alan_zimmers 2006-01-19T11:24:03ZAdverseEvent Anthrax Vaccination&Treatment Anthrax Vaccination&Treatment Alan R Alan R ns#AnthraxTreatment ns#AnthraxTreatment ns#HomelandSeccurity ns#HomelandSeccurity This research suggests a lower limit of adverse responses This research suggests a lower limit of adverse responses Female service members reported higher rates of reactions to the previous dose of vaccine during anthrax vaccination of all U.S. military personnel. Female service members reported higher rates of reactions to the previous dose of vaccine during anthrax vaccination of all U.S. military personnel. A Sainz-Perez A Sainz-Perez H Gary-Gouy H Gary-Gouy 16408101 16408101 PMID: 16408101 PMID: 16408101 2006-01-12 2006-01-12 Leukemia Leukemia 0887-6924 0887-6924 Anthrax Vacc N251 Alan R ns#AnthraxTreatment ns#HomelandSecurity nugget expert topic kChannel

79 79 Knowledge Channels High Mda-7 expression promotes malignant cell survival and p38 MAP kinase activation in chronic lymphocytic leukemia. High Mda-7 expression promotes malignant cell survival and p38 MAP kinase activation in chronic lymphocytic leukemia. http://www.connotea.org/user/hannahr/uri/48e905bdb66310af85ad2e8503628e01 Posted by hannahr to CLLSignalling&Processes on Thu Jan 19 2006 Posted by hannahr to CLLSignalling&Processes on Thu Jan 19 2006 hannahr2006-01-19T11:24:03ZCLLSignalling&Processes High Mda-7 expression promotes malignant cell survival and p38 MAP kinase activation in chronic lymphocytic leukemia. High Mda-7 expression promotes malignant cell survival and p38 MAP kinase activation in chronic lymphocytic leukemia. A Sainz-Perez A Sainz-Perez H Gary-Gouy H Gary-Gouy 16408101 16408101 PMID: 16408101 PMID: 16408101 2006-01-12 2006-01-12 Leukemia Leukemia 0887-6924 0887-6924

80 80 Knowledge Channels High Mda-7 expression promotes malignant cell survival and p38 MAP kinase activation in chronic lymphocytic leukemia. High Mda-7 expression promotes malignant cell survival and p38 MAP kinase activation in chronic lymphocytic leukemia. http://www.connotea.org/user/hannahr/uri/48e905bdb66310af85ad2e8503628e01 http://www.connotea.org/user/hannahr/uri/48e905bdb66310af85ad2e8503628e01 Posted by hannahr to CLLSignalling&Processes on Thu Jan 19 2006 Posted by hannahr to CLLSignalling&Processes on Thu Jan 19 2006 hannahr hannahr 2006-01-19T11:24:03Z 2006-01-19T11:24:03Z CLLSignalling&Processes CLLSignalling&Processes Alan R Alan R ns#P38 ns#P38 ns#Kinases ns#Kinases This paper suggests a mechanism for P38 protection of CLL B-cells This paper suggests a mechanism for P38 protection of CLL B-cells High Mda-7 expression promotes malignant cell survival and p38 MAP kinase activation in chronic lymphocytic leukemia. High Mda-7 expression promotes malignant cell survival and p38 MAP kinase activation in chronic lymphocytic leukemia. A Sainz-Perez A Sainz-Perez H Gary-Gouy H Gary-Gouy 16408101 16408101 PMID: 16408101 PMID: 16408101 2006-01-12 2006-01-12 Leukemia Leukemia 0887-6924 0887-6924 P38 paper N251 Alan R ns#P38 ns#Kinases nugget expert topic kChannel

81 81 Surveillance using RSS/RDF (CDC)

82 82 Clinical Data Standards (CDISC)

83 83 Protocol and the Semiotic Triangle Doug Fridsma (U Pittsburg ) Symbol Protocol We need to sign off on the protocol by Friday Concept 1 Thing 1 Document Study Protocol XYZ has enrolled 73 patients Thing 2 Concept 2 Per the protocol, you must be at least 18 to be enrolled Concept 3 Thing 3 Plan Source: John Speakman/Charlie Mead

84 84 CDISC and the Semantic Web? Reduce the need to write data parsers to any CDISC XML SchemaReduce the need to write data parsers to any CDISC XML Schema Make use of ontologies and terminologies directly using RDFMake use of ontologies and terminologies directly using RDF Easier inclusion of Genomic dataEasier inclusion of Genomic data Use Semantic Lenses for ReviewersUse Semantic Lenses for Reviewers Easier acceptance by industry with their current technologiesEasier acceptance by industry with their current technologies

85 85 During 2006-2007 HL7 HL7 Health Level Seven CDISC CDISC Clinical Data Interchange Standards Consortium Relationship HL7/CDISC RCRIM Regulated Clinical Research and Information Management, technical committee SDTM variables as Common Data Elements & Controlled Terminologies UMLS NCI ThesaurusIn OWL format BRIDG BRIDG Biomedical Research Integrated Domain Group Model

86 86 Ongoing work at FDA HL7 HL7 Health Level Seven CDISC CDISC Clinical Data Interchange Standards Consortium Relationship HL7/CDISC RCRIM Regulated Clinical Research and Information Management, technical committee Announcement of CDISC/SDTM as a standard format Janus Model and Data Warehouse "The FDA has the largest pool of randomized clinical trial data in the world, but it cannot be analyzed now because it is inaccessible" Dr. Janet Woodcock, Deputy Commissioner for Operations and Chief Operating Officer, FDA 27 January 2006 … populate a cross-study database and do more comprehensive analyses for the benefit of patients.

87 87 Retrospective DBs: JANUS Accessing and analyzing CT data without changing any schemaAccessing and analyzing CT data without changing any schema Interpretive Annotations without impacting CDBInterpretive Annotations without impacting CDB Analyses and Insights by Future ProjectsAnalyses and Insights by Future Projects Collective Knowledge on Targets, Diseases, and ToxicitiesCollective Knowledge on Targets, Diseases, and Toxicities

88 88 Tox Commons Proposed Open Re-use of Failed CompoundsProposed Open Re-use of Failed Compounds Common Effort by PharmaceuticalsCommon Effort by Pharmaceuticals Puzzle AnalogyPuzzle Analogy No real IP in Failed Clinical DataNo real IP in Failed Clinical Data Part of Science Commons InitiativePart of Science Commons Initiative Is a Drug Safety Commons Possible?, Bio-ITWorldIs a Drug Safety Commons Possible?, Bio-ITWorld

89 89 FDAs JANUS Full Model one visual representation

90 90 FDAs JANUS basic elements diagram another visual representation General classes of Clinical observations

91 91 SDTM ala RDF a cdisc:Subject ; a cdisc:Subject ; nci:sex_code nci:Female ; nci:sex_code nci:Female ; cdisc:treatment ; cdisc:vitalSigns ; cdisc:adverseEvent ; cdisc:treatment ; cdisc:vitalSigns ; cdisc:adverseEvent ; // ROUTE DRGGROUP DOSE pid treatment tpfday tptday // IV B 7 MG 4183542663506 7mg then 14mg SEMWEB 6/11/84 7/11/84 a cdisc:Treatment ; // cdisc:Treatment is a subclass of cdisc:Observation cdisc:design_arm ; cdisc:route cdisc:IV_route ; a cdisc:Treatment ; // cdisc:Treatment is a subclass of cdisc:Observation cdisc:design_arm ; cdisc:route cdisc:IV_route ; cdisc:drug_group "B; cdisc:drug_group "B; cdisc:dose "7" ; cdisc:dose "7" ; cdisc:dose_units nist:mg ; cdisc:dose_units nist:mg ; cdisc:treatment "7mg then 14mg SEMWEB" ; cdisc:treatment "7mg then 14mg SEMWEB" ; cdisc:first_date "6/11/84" ; cdisc:first_date "6/11/84" ; cdisc:term_date "7/11/84". cdisc:term_date "7/11/84".

92 92 SDTM ala RDF a cdisc:Biomarker_Measure ; // a subclass of cdisc:Observation cdisc:biomarker_proc ; cdisc:mol_analyses nih:gene_expression ; a cdisc:Biomarker_Measure ; // a subclass of cdisc:Observation cdisc:biomarker_proc ; cdisc:mol_analyses nih:gene_expression ; cdisc:biomarker_set ; cdisc:biomarker_set ; cdisc:biomarker_values [2.343, 1.211, 0531, 23.34, 83.12, 4.323, 9.543] ; cdisc:biomarker_values [2.343, 1.211, 0531, 23.34, 83.12, 4.323, 9.543] ; cdisc:units nist:norm_ratio ; cdisc:units nist:norm_ratio ; cdisc:date "6/11/84" ; cdisc:date "6/11/84" ;

93 93 HCLS Drug Safety and Efficacy Focus Areas Translational Science PerspectiveTranslational Science Perspective – Subject State Thinking (biomarkers) Safety dimensionsSafety dimensions Efficacy (disease models)Efficacy (disease models) – Animal Human (CDISCs SEND, SDTM/ODM) Clinical Observations and their relation to biomarkers (+ mechanisms) and pharmacogenomicsClinical Observations and their relation to biomarkers (+ mechanisms) and pharmacogenomics Connecting back to DiscoveryConnecting back to Discovery – Targets – Biomarkers – Therapeutic Knowledge – Leads, Candidates selection – Mechanisms of Action BioPAX…BioPAX…

94 94 Proposed Notes and Activities http://www.w3.org/2001/sw/hcls/ Notes plannedNotes planned – SDTM and JANUS from a SW perspective Semantic enriched evolvable recombinant clinical observationsSemantic enriched evolvable recombinant clinical observations DEMO: Table and XML models ala RDFDEMO: Table and XML models ala RDF – Retrospective DBs (JANUS) and SW + power of annotations and links DEMO: using URI code and RDBMDEMO: using URI code and RDBM – Provenance and trust (non-reputability) ACL?ACL?

95 95 Reasons for SW Exponential Growth and Distribution of Medical Knowledge and Complex Data - needs to scale with the Web!Exponential Growth and Distribution of Medical Knowledge and Complex Data - needs to scale with the Web! Reduce innovation adoption curve from discovery into accepted standards of practice (currently 17 years)Reduce innovation adoption curve from discovery into accepted standards of practice (currently 17 years) Reduce the cost/duration/risk of clinical trial managementReduce the cost/duration/risk of clinical trial management –Patient identification and recruitment –Trial Design (Learn/Confirm, adaptive trials) –Improved data quality and clinical outcomes measurement –Post-market surveillance (knowledge channels) Reduce preventable, anticipatable adverse events (5-10%)Reduce preventable, anticipatable adverse events (5-10%) The market is balking at healthcare inflation, new technologies and therapeutics will find increasing resistance for reimbursementThe market is balking at healthcare inflation, new technologies and therapeutics will find increasing resistance for reimbursement –SW could prove many time less expensive than traditional IT solutions –Less code creation and maintenance if Rules are SW based SW content is more manageable to achieve business goalsSW content is more manageable to achieve business goals

96 96 Key Semantic Web Principles Plan for change Free data from the application that created it Lower reliance on overly complex Middleware The value in "as needed" data integration Big wins come from many little ones The power of links - network effect Open-world, open solutions are cost effective Importance of "Partial Understanding"

97 Thank You More info at http://www.w3.org/2001/sw/hcls/


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