Clinical Observations Interoperability: - Bridging Clinical Practice and Clinical Research

Clinical Observations Interoperability: - Bridging Clinical Practice and Clinical Research

Helen Chen

February 13, 2009

2 Helen Chen Feb. 13, 2009

Motivation

• Pressing need for interoperability between clinical trials and clinical practice

• Bridge gaps between various standards to connect related fields• Semantic Web technologies are maturing quickly and getting ready

for prime time


Clinical Observation Interoperability Group

• W3C Semantic Web for Life Sciences and Healthcare Interest Group

• Members from CDISC, clinical trial researchers and healthcare IT researchers

• http://esw.w3.org/topic/HCLS/ClinicalObservationsInteroperability


Goals

• Show that Semantic Web technologies can be used to bridge the gap between the two• Map across formats• Reuse existing data

• Develop a proof of concept application that demonstrates the feasibility of that approach


Use Case Step-Through

1. (Textual) specification of the eligibility criteria for a given clinical trial

2. Ontology-based translation of the eligibility criteria into SPARQL queries

3. Translation of the SPARQL queries into database-specific queries

4. Execution of the queries at the databases) –results contain all eligible patients

5. Return of a list of eligible patients to clinical trial administrator


Problems

1. Data is not stored only in form of the CDISC SDTM but also in HL7 DCM form

2. Concepts in SDTM domain do not always have one-one mapping to HL domain

3. Drug prescription coded in different drug codes (RxNorm and NDC)

4. Data is not stored as RDF but in conventional relational databases


Solution

• Transform references described in SDTM classes to references described to HL7 classes via N3 rules

• Obtain relavant concepts at different granularity via inferencing• Class->subclasses

• Derive new classes (weight loss drug) via <may-treat> mechanism

• Transform either…• Relational data into RDF

• or SPARQL into SQL via N3 rules

• Second approach chosen


Ontology Modeling

• Develop ontologies for clinical trials and clinical practice based on CDISC SDTM and HL7 DCM

• Write mapping rules• Model patient and medication aspects…

• Lab results and observations• Patient vital signs• Medications


Inclusion Criteria

Type 2 diabetes on diet and exercise therapy or monotherapy with metformin, insulin secretagogue, or alpha-glucosidase inhibitors, or a low-dose combination of these at 50% maximal dose. Dosing is stable for 8 weeks prior to randomization.

?patient :takes :metformin ... ..


Exclusion Criteria

Use of warfarin (Coumadin), clopidogrel (Plavix) or other anticoagulants..

?patient doesNotTake anticoagulant .

.. ..


Medication

:M0271 a sdtm:Medication; spl:classCode 6809 ; #metformin sdtm:subject :P0006; sdtm:dosePerAdministration [ sdtm:hasValue 500; sdtm:hasUnit "mg„ ]; sdtm:startDateTime "20070101T00:00:00"^^xsd:dateTime ; sdtm:endDateTime "2008-0101T00:00:00"^^xsd:dateTime .


Criteria in SPARQL

?medication1 sdtm:subject ?patient ;spl:activeIngredient ?ingredient1 .

?ingredient1 spl:classCode 6809 . OPTIONAL { ?medication2 sdtm:subject ?patient ;

spl:activeIngredient ?ingredient2 .?ingredient2 spl:classCode 11289 .

} FILTER (!BOUND(?medication2))

metformin

anticoagulant

Exclusion Criteria


Drug Class Information in CT #8

• monotherapy with metformin, insulin secretagogue, or alpha-glucosidase inhibitors and a low dose combination of all

• Long term insulin therapy

• Therapy with rosiglitazone (Avandia) or pioglitazone (Actos), or extendin-4 (Byetta), alone or in combination

• corticosteroids

• weightloss drugs e.g., Xenical (orlistat), Meridia (sibutramine), Acutrim (phenylpropanol-amine), or similar medications

• nonsteroidal anti-inflammatory drugs

• Use of warfarin (Coumadin), clopidogrel (Plavix) or other anticoagulants

• Use of probenecid (Benemid, Probalan), sulfinpyrazone (Anturane) or other uricosuric agents


Prescription Information in Patient Database

• "132139","131933","98630 ","GlipiZIDE-Metformin HCl 2.5-250 MG Tablet","54868079500 ",98630,"2.5-250 ","TABS","","MG "," ","15","GlipiZIDE-Metformin HCl ","","GlipiZIDE-Metformin HCl 2.5-250 MG Tablet“

• "132152","131946","98629 ","GlipiZIDE-Metformin HCl 2.5-500 MG Tablet","54868518802 ",98629,"2.5-500 ","TABS","","MG "," ","15","GlipiZIDE-Metformin HCl ","","GlipiZIDE-Metformin HCl 2.5-500 MG Tablet“

• "132407","132201","98628 ","GlipiZIDE-Metformin HCl 5-500 MG Tablet","54868546702 ",98628,"5-500 ","TABS","","MG "," ","15","GlipiZIDE-Metformin HCl ","","GlipiZIDE-Metformin HCl 5-500 MG Tablet“

• "132642","132436","C98630 ","GlipiZIDE-Metformin HCl TABS","54868079500 ",98630,"","TABS",""," "," ","15","GlipiZIDE-Metformin HCl ","","GlipiZIDE-Metformin HCl TABS"

NDC Code


Drug Ontology By Stanford

from drug ontology documentation


NDC:54868079500: GlipiZIDE-Metformin HCl 2.5-250 MG Tablet

NDC: 54868518802: GlipiZIDE-Metformin HCl 5-500 MG Tablet

NDC:54868079500:GlipiZIDE-Metformin HCl TABS

CTmetformin,

insulin secretagogue

alpha-glucosidase inhibitors

anticoagulants

uricosuric agents

nonsteroidal anti-inflammatorydrugBank: DB00331RxNORM: 6809C0025598

Mapping Between CT and Patient Record

Drug Ontology

MechanismOfAction

GeneralDrugType

C1299007

C0066535

C0050393


SDTM to HL7 Transformation

hl7hl7:Substance- :Substance- Administration Administration

hl7hl7:doseQuantity:doseQuantity

{ { ?x a ?x a sdtmsdtm:Medication ;:Medication ; sdtmsdtm:dosePer- :dosePer- Administration ?y Administration ?y} => {} => { ?x ?x hl7hl7:Substance-:Substance- Administration ; Administration ; hl7hl7:doseQuantity ?y:doseQuantity ?y}}

sdtmsdtm:Medication:Medication

sdtmsdtm:dosePer-:dosePer- Administration Administration

Clinical Trial Ontology

Clinical Practice Ontology


HL7 to EMR Database Transformation

hl7hl7:Substance- :Substance- Administration Administration

hl7hl7:doseQuantity:doseQuantity

{ { hl7:substanceAdministration hl7:substanceAdministration [[

aa hl7:SubstanceAdministration ;hl7:SubstanceAdministration ; hl7:consumable [hl7:consumable [

hl7:displayNamehl7:displayName ?takes ; ?takes ; spl:activeIngredient [spl:activeIngredient [

spl:classCode ?ingredspl:classCode ?ingred ]]] ;} => {] ;} => {

{{?indicItem Item_Medication:PatientID ?person;?indicItem Item_Medication:PatientID ?person; Item_Medication:PerformedDTTM Item_Medication:PerformedDTTM

?indicDate ;?indicDate ; Item_Medication:EntryNameItem_Medication:EntryName ? ?takes .takes .

..}}

SPARQL in Clinical Practice Ontology

SQL to EMR Database

Item_MedicationItem_Medication:EntryName:EntryName ?takes . ?takes .

MedicationMedication:ItemID:ItemID ?indicItem; ?indicItem;


Pushing Query to Database

• SPARQL in SDTM ontology to SPARQL in HL7 ontology• SPARQL in HL7 ontology to SQL in EMR database

CT

Eligibility

HL

7 DC

M/R

IM

EM

R

SPARQL SQLSPARQL

List of eligible patients


SPARQL in SDTM

PREFIX sdtm: <http://www.sdtm.org/vocabulary#>PREFIX spl: <http://www.hl7.org/v3ballot/xml/infrastructure/vocabulary/vocabulary#>

SELECT ?patient ?dob ?sex ?takes ?indicDate?contra WHERE { ?patient a sdtm:Patient ; sdtm:middleName ?middleName ; sdtm:dateTimeOfBirth ?dob ; sdtm:sex ?sex . [ sdtm:subject ?patient ;

sdtm:standardizedMedicationName ?takes ; spl:activeIngredient [ spl:classCode ?code ] ;

sdtm:startDateTimeOfMedication ?indicDate ] . OPTIONAL { [ sdtm:subject ?patient ;

sdtm:standardizedMedicationName ?contra ; spl:activeIngredient [ spl:classCode 11289 ] ;

sdtm:effectiveTime [ sdtm:startDateTimeOfMedication ?contraDate ] . } FILTER (!BOUND(?contra) && ?code = 6809)}


SDTM-HL7 Mapping Rules

CONSTRUCT {?patient a sdtm:Patient ; sdtm:middleName ?middleName ; sdtm:dateTimeOfBirth ?dob ; sdtm:sex ?sex .

[ a sdtm:ConcomitantMedication ;sdtm:subject ?patient ;sdtm:standardizedMedicationName ?takes ;spl:activeIngredient [ spl:classCode ?ingred ] ;sdtm:startDateTimeOfMedication ?start

] .} WHERE {?patient a hl7:Person ;

hl7:entityName ?middleName ; hl7:livingSubjectBirthTime ?dob ; hl7:administrativeGenderCodePrintName ?sex ; hl7:substanceAdministration [

a hl7:SubstanceAdministration ; hl7:consumable [

hl7:displayName ?takes ; spl:activeIngredient [ spl:classCode ?ingred ]

] ;hl7:effectiveTime [ hl7:start ?start ]

] .}


SPARQL in HL7 Via SWtranformer

PREFIX hl7: <http://www.hl7.org/v3ballot/xml/infrastructure/vocabulary/vocabulary#>

SELECT ?patient ?dob ?sex ?takes ?indicDate WHERE{ ?patient hl7:entityName ?middleName . ?patient hl7:livingSubjectBirthTime ?dob . ?patient hl7:administrativeGenderCodePrintName ?sex . ?patient a hl7:Person . ?patient hl7:substanceAdministration ?b0035D918_gen0 . ?b0035D918_gen0 hl7:consumable ?b0035C798_gen1 . ?b0035D918_gen0 a hl7:SubstanceAdministration> . ?b0035D918_gen0 hl7:effectiveTime ?b0035C5E8_gen3 . ?b0035C798_gen1 hl7:displayName ?takes . ?b0035C798_gen1 hl7:activeIngredient ?b0035C848_gen2 . ?b0035C848_gen2 hl7:classCode ?code . ?b0035C5E8_gen3 hl7:start ?indicDate . FILTER ( ?code = 6809 )}


HL – Database Mapping Rules: Tables

PREFIX xsd: <http://www.w3.org/2001/XMLSchema#>PREFIX Person: <http://hospital.example/DB/Person#>PREFIX Sex_DE: <http://hospital.example/DB/Sex_DE#>PREFIX Item_Medication: <http://hospital.example/DB/Item_Medication#>PREFIX Medication: <http://hospital.example/DB/Medication#>PREFIX Medication_DE: <http://hospital.example/DB/Medication_DE#>PREFIX NDCcodes: <http://hospital.example/DB/NDCcodes#>


HL – Database Mapping Rules: Schema

CONSTRUCT { ?person a hl7:Person ; hl7:entityName ?middleName ; hl7:livingSubjectBirthTime ?dob ; hl7:administrativeGenderCodePrintName ?sex ; hl7:substanceAdministration [ a hl7:SubstanceAdministration ; hl7:consumable [

hl7:displayName ?takes ; spl:activeIngredient [ spl:classCode ?ingred]

] ; hl7:effectiveTime [ hl7:start ?indicDate ]

] . } WHERE {

?person Person:MiddleName ?middleName ; Person:DateOfBirth ?dob ; Person:SexDE ?sexEntry .

OPTIONAL { ?indicItem Item_Medication:PatientID ?person ; Item_Medication:PerformedDTTM ?indicDate ; Item_Medication:EntryName ?takes . ?indicMed Medication:ItemID ?indicItem ; Medication:DaysToTake ?indicDuration ; Medication:MedDictDE ?indicDE . ?indicDE Medication_DE:NDC ?indicNDC . } }


SQL to Database

SELECT patient.id AS patient, patient.DateOfBirth AS dob, sexEntry_gen0.EntryName AS sex, indicItem_gen1.EntryName AS takes, indicItem_gen1.PerformedDTTM AS indicDate

FROM Person AS patientINNER JOIN Sex_DE AS sexEntry_gen0 ON sexEntry_gen0.id=patient.SexDE INNER JOIN Item_Medication AS indicItem_gen1 ON indicItem_gen1.PatientID=patient.id

INNER JOIN Medication AS indicMed_gen2 ON indicMed_gen2.ItemID=indicItem_gen1.id

INNER JOIN Medication_DE AS indicDE_gen5 ON indicDE_gen5.id=indicMed_gen2.MedDictDE

INNER JOIN NDCcodes AS indicCode_gen6 ON indicCode_gen4.ingredient=6809 AND indicCode_gen6.NDC=indicDE_gen5.NDC


COI Demo

• coi svn: • http://code.google.com/p/coi/source/checkout

• Public access: (working in progress)• http://hcls.deri.org/coi/demo/


COI Demo – Clinical Trial Eligibility Criteria


COI Demo – Selecting Inclusion Criteria

Inclusion in SDTM ontology

SDTM clinical trial ontology


COI Demo – Drug Ontology Inference

Exclusion in Drug ontology

Drug ontologySubcla

sses o

f “an

ticoag

ulant”


COI Demo – Selecting Mapping Rules

#check all drugs that "may_treat obese" {?A rdfs:subClassOf ?B; rdfs:label ?D. ?B a owl:Restriction; owl:onProperty :may_treat; owl:someValuesFrom :C0028754} => {?D a :WeightLoseDrug}.


COI Demo – Getting Right Patients


Benefits of Semantic Web Apporach

• Unambiguious conceptual model for seperate doamins without early commitment to a common ontology

• Reusable mapping rules from clinical research domain to Healthcare domain

• Late bining of coding systems and database schema• Transform SPARQL to SQL in real time, reflecting real time discovery

and integration needs


Challenges Ahead

• Large Tree Structure Navigation• Reasoning on Large Data• SWTranformer, url processing and “filter” clause implementation• Portability Issues on DERI Server• More patient data to test all proposed scenarios

Questions and Suggestions?

Thank You!

Documents

Clinical Observations Interoperability: - Bridging Clinical Practice and Clinical Research