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Pharmacogenomics Data Standardization using Clinical Element Models

Pharmacogenomics Data Standardization using Clinical Element Models. Pharmacogenomics Ontology (PHONT) Network Resource. Pharmacogenomics Research Network (PGRN) Diverse network of PGx research sites Goal: Understand how genetic variations affect an individual's response to medications

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Pharmacogenomics Data Standardization using Clinical Element Models

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  1. Pharmacogenomics Data Standardization using Clinical Element Models

  2. Pharmacogenomics Ontology (PHONT) Network Resource • Pharmacogenomics Research Network (PGRN) • Diverse network of PGx research sites • Goal: Understand how genetic variations affect an individual's response to medications • Normalize data representations • Disease phenotypes • Drugs and drug classes

  3. UMLS Semantic Types PGRN Data Dictionary Standardization • 4483 PGRN Variables • SHARPn CEMs: Patient, Noted Drug, Disease/Disorder, Lab Observation

  4. Categories of Mapped Variables

  5. Person Model Examples of Variables Person Medical Record Number PatientExternalId (0-M) SSN data (II) Study ID PersonName (1-M) First Name GivenName (0-1) Last Name data (ST) … Date of Birth Birthdate (0-1) Year of Birth data (TS) AdministrativeGender (0-1) Patient Gender data (CD) Patient Race AdministrativeRace (0-1) AdministrativeEthnicGroup (0-1) Self-Reported Ethnicity …

  6. Drug Administration Model Examples of Variables NotedDrug Code Is the patient taking a diuretic? data (CD) Has the subject started any new medications? StartTimeUnconstrained data (TS/CD/ST) Date of last antihypertensives EstimatedInd data (CO) Medication start date TakenDoseLowerLimit Dose Have you taken digoxin in the past? data (PQ) RouteMethodDevice data (CD) Time on tamoxifen StatusChange If potassium supplementation added, specify daily dose Subject …

  7. Example: Patient Took 300 mg Acetaminophen "300"

  8. Categories of Unmapped Variables

  9. Unmapped Variables • Some variables are not currently represented by PHONT (SHARP) CEMs • Computed research data (e.g., PK/PD) • Genomic data • Psychometric data • Work with SDOs to address these gaps • CIMI community on extant or new CEMs • HL7 and CDISC for clinical genomics data • W3C, NLM, & SNOMED PGx ontologies

  10. Conclusions • Demonstrated CEMs can be used to normalize PGRN data dictionaries • Future Work • Incorporate recently developed SHARP CEMs • Collaborate with SHARP to fill gaps for PGx • Establish best practices • Complex data elements (e.g., semantic links) • Project-specific/workflow data vs EMR

  11. Scientific Christopher G. Chute Robert R. Freimuth Jyotishman Pathak Qian Zhu Guoqian Jiang Nosologist Donna Ihrke IT Zonghui Lian Scott Bauer Deepak Sharma Project Management Mandy Ager Matthew Durski PHONT Personnel

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