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Automatic Mapping of Drug Concepts to
the RxNorm Vocabulary
Maxim Moinat* [1], Lars Pedersen [2], Jolanda Strubel [1], Marinel Cavelaars [1], Kees van Bochove [1], Peter Rijnbeek [3], Michel van
Speybroeck [4], Martijn Schuemie [4]
[1] The Hyve, Utrecht, The Netherlands
The Hyve, Cambridge, United States
[2] Aarhus University Hospital, Aarhus, Denmark
[3] Erasmus MC, Rotterdam, The Netherlands
[4] Janssen Pharmaceuticals, Inc.
*E-mail: maxim@thehyve.nl.
1. Background
Mapping source concepts to the standard concepts in the OMOP vocabularies is one of the most
time-consuming tasks during the transformation to the OMOP Common Data Model. Drug mapping is in
particular challenging, because different components have to be mapped: ingredient, dose form and
strength.
As part of the European Medical Information Framework (EMIF) project, Danish population health data are
mapped to the OMOP CDM, including the local drug codes. The Hyve assists in creating a script to
automatically map a set of 4754 drugs to the RxNorm vocabulary. The input data contains ATC codes,
dosage forms, numerical strengths and strength units. Two examples are shown in Figure 1.
The mapping procedure presented here is based on the drug mapping for the Japan Medical Data Center
Claims DatabaseI
.
We empower scientists by building
on open source software
2. Mapping Procedure
The mapping uses the RxNorm hierarchy and consists of four steps (see Figure 2).
1. Drugs are mapped to RxNorm Ingredient via the 5th
level ATC code. The OMOP
relationship ‘ATC - RxNorm’ is used for this purpose.
2. Dose form is added to the ingredient level, to map to Clinical Drug Form level.
3. The information on drug strength (including unit) is added to map to Clinical
▲ Figure 1: Examples of input data. Example 1 is successfully
mapped automatically. Example 2 consists of two ingredients and has
an ATC concept that could not be mapped to a RxNorm concept.
➢ Risperdal
➢ N05AX08
➢ Filmovertrukne tabletter
➢ 0.5
➢ MG
Example 1
Example 2
➢ Fortzaar
➢ C09DA06
➢ depottabletter
➢ 100 + 25 mg
Risperidone 0.5 MG Oral Tablet
(RxNorm Clinical Drug)
Condesartan and diuretics
(ATC code)
Mapped to](https://image.slidesharecdn.com/2016-01-26-scopesummitohdsi-keesvanbochove-thehyve-170213204833/75/SCOPE-Summit-Applying-the-OMOP-data-model-OHDSI-software-to-national-European-health-data-registries-the-IMI-EMIF-project-47-2048.jpg)
Uploaded byKees van Bochove
SCOPE Summit - Applying the OMOP data model & OHDSI software to national European health data registries: the IMI EMIF project
The document discusses the role of The Hyve in fostering an open-source community for real-world data analysis, highlighting the OHDSI initiative, which aggregates data from over 650 million patients for observational research. It covers the OMOP data model used for standardizing healthcare data and presents the tools developed for analytics and mapping processes. Additionally, it outlines the European Medical Information Framework (EMIF) as a key platform for healthcare data intelligence and insights into disease treatment practices.
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SCOPE Summit - Applying the OMOP data model & OHDSI software to national European health data registries: the IMI EMIF project
- 1. Open source communityfor “Real World Data” Analysis JANUARY 26, 2017, SCOPE SUMMIT, MIAMI Kees van Bochove, CEO & Founder, The Hyve – @keesvanbochove With thanks to Patrick Ryan, Nigel Hughes & Bart Vannieuwenhuyse from Janssen for slides & feedback!
- 2. 2 Agenda 1. Introduction: TheHyve & Open Source 2. What’s OHDSI & what can it do for you? 3. Under the hood: OMOP Data Model & Mapping Process 4. Showcase: OHDSI data analytics tools 5. The application of OMOP and OHDSI in IMI EMIF
- 3.
- 4.
- 5. Interdisciplinary team so5ware engineers, data scien1sts, project managers & staff; exper1se in bioinforma1cs, medical informa1cs, so5ware engineering, biosta1s1cs etc. 5
- 6. Open Source u Sourcecode openly accessible and reusable for everyone u Enables pre-competitive collaboration: both academics and industry can use and enhance it u Transparency: verification (scientific as well as IT security) can be done by anyone, no ‘black box’
- 7. 7 3 Health DataAreas The Hyve is active in u Translational Research Data (‘Clinical & bioinformatics data’) u Population Health Data (‘Real world data’) u Personal Health Data (‘Mobile & sensors data’) Example (RWD) projects:
- 8. 2. WHAT IS OHDSI? OBSERVATIONALHEALTH DATA SCIENCES AND INFORMATICS 8
- 9.
- 10. 10 What is OHDSIto you? u OHDSI is a scientific community to develop best practices for observational research studies u OHDSI is a data network bringing together data from over 650 million patients worldwide to execute studies u OMOP is an open data model and OHDSI is a suite of open source software tools for analysis (epidemiology, but also e.g. inclusion/exclusion criteria feasibility)
- 11. 11 Questions OHDSI cananswer given a set of patient journeys
- 12. 12 Questions OHDSI cananswer Clinical characterization Population-level effect estimation Patient-level prediction Which treatment did patients choose after diagnosis? Which patients chose which treatments? How many patients experienced the outcome after treatment? Does one treatment cause the outcome more than an alternative? Does treatment cause outcome? What is the probability I will develop the disease? What is the probability I will experience the outcome?
- 13. 13 Questions OHDSI cananswer
- 14. 14 How are patientswith major depressive disorder treated in real world data (250M)? http://bit.ly/2jYCGkI
- 15. 15 Informing Clinical TrialDesign u Designing and testing inclusion/exclusion criteria for trials u Performing observational studies as a basis for choosing effective randomized clinical trial designs and targets u Elucidating real world use of medicines and treatments for safety purposes
- 16. 3. UNDER THE HOOD THEOMOP DATA MODEL & MAPPING PROCESS 16
- 17. 17 OMOP & OHDSITools - Overview u OMOP: Common Data Model for observational healthcare data: persons, drugs, procedures, devices, conditions etc. u OHDSI: Large-scale analytics tools for observational data An open source community, a.o. developing: u Tools to support the ETL / mapping process into OMOP (White Rabbit etc.) u Tools to perform analytics: e.g. Achilles for data profiling, Calypso for feasibility assessment à now being integrated into ATLAS www.omop.org www.ohdsi.org
- 18. 18 OMOP Common DataModel v5.0 v OMOP = Observational Medical Outcomes Partnership v CDM = Common Data Model v SQL Tables
- 19.
- 20.
- 21. 21 Mapping the sourcedata to OMOP CDM ETL design ETL implementation White Rabbit Source data inventarisation Rabbit in a Hat Map source tables to CDM structure Toolsused Usagi Map source terms to CDM ontologies (vocabulairies) syntactic mapping semantic mapping ETL verification Achilles Review database profiles Review data quality assesment (Achilles Heel)
- 22. 22 Output from WhiteRabbit Tab “Overview”: fields for each table Tab “Medication”: per table values in fields and frequencies =Medication name
- 23. 23 Mapping of tablesto CDM
- 24. 24 v All codeditems (gender, race etc) need to be mapped v Mapping of Medication, Diagnosis, procedures values to appropriate ontology (RXNorm, ICD-9 etc) Map terms to target vocabularies NHANES Gender code NHANES Gender description Equivalent OMOP SOURCE_CODE OMOP SOURCE_CODE_DESCRIP TION SOURCE_TO_CONCEPT_M AP_ID . missing U UNKNOWN 8551 1 Male M MALE 8507 2 Female F FEMALE 8532
- 25. 25 Overview of ontologiesused in OMOP over 80 healthcare vocabularies mapped
- 26. 4. OHDSI – ANALYTICSTOOLS 26
- 27.
- 28. 28 Work with thecommunity
- 29.
- 30. 30 What can Ido with OHDSI tools? u Explore & QC the mapped data u Build cohort definitions using concept sets u Look at patient profiles u Run and evaluate queries for clinical study feasibility assesment
- 31.
- 32.
- 33.
- 34.
- 35. 35 ATLAS: Concept SetDefinition
- 36.
- 37. 37 ATLAS: Individual PatientProfile
- 38. 38 Inclusion/Exclusion Query Results Slidefrom P. Ryan, Janssen
- 39. 5. IMI EUROPEAN MEDICALINFORMATION FRAMEWORK 39
- 40. To become thetrusted European hub for health care data intelligence, enabling new insights into diseases and treatments EMIF vision 40 Discover Assess Reuse
- 41. The real storyof the treatments in clinical practice 41 The value of healthcare data for secondary uses in clinical research and development — Gary K. Mallow, Merck, HIMSS 2012 1 2 3 4 5 6 7 8 9 1,000 10,000 100,000 1 million Years #PatientExperiences/Records The “burning platform” for life sciences Pharma-owned highly controlled clinical trials data Clinical practice, patients, payers and providers own the data Product Launch R&D Phase IV Challenge Today, Pharma doesn’t have ready access to this data, yet insights for safety, CER and other areas are within this clinical domain, which includes medical records, pharmacy, labs, claims, radiology etc.
- 42. Data available throughEMIF consortium § Large variety in “types” of data § Data is available from more than 53 million subjects from seven EU countries, including Primary care data sets Hospital data Administrative data Regional record- linkage systems Registries and cohorts (broad and disease specific) Biobanks >25,000 subjects in AD cohorts >90,000 subjects in metabolic cohorts
- 43. 43 EMIF Platform Design Data access Module Data access Module Extract SiteY Site Z Extract CommonOntology/De-identification EMIF platform solution Governance Data owners Researchers User admin User admin Remote user 1 Remote user 2 Data Sources 1° care Hospital Admin Regional Registries & cohorts Biobanks 2° care Paediatric
- 44. >40million MAAS SDR EGCUT PEDIANET SCTS IMASIS HSD AUH IPCI ARS SIDIAP PHARMO THIN 100 1,000 10,000100,000 1,000,000 10,000,000 100,000,000 Approximatetotal(cumulative)numberofsubjects Available data sources in EMIF 44 EMIF-Platform EMIF-Available Data Sources; EXAMPLES 1K 2K 52K 400K 475K 2.8M 2.3M 10M Status Jan 2016 3.6M 1.6M 1M 12M 6M
- 45. Catalogue with availabledata sources 45 https://emif-catalogue.eu Just released last week! see www.emif.eu
- 46. Catalogue with availabledata sources 46 https://emif-catalogue.eu
- 47. 47 Automatic Mapping ofDrug Concepts to the RxNorm Vocabulary Maxim Moinat* [1], Lars Pedersen [2], Jolanda Strubel [1], Marinel Cavelaars [1], Kees van Bochove [1], Peter Rijnbeek [3], Michel van Speybroeck [4], Martijn Schuemie [4] [1] The Hyve, Utrecht, The Netherlands The Hyve, Cambridge, United States [2] Aarhus University Hospital, Aarhus, Denmark [3] Erasmus MC, Rotterdam, The Netherlands [4] Janssen Pharmaceuticals, Inc. *E-mail: maxim@thehyve.nl. 1. Background Mapping source concepts to the standard concepts in the OMOP vocabularies is one of the most time-consuming tasks during the transformation to the OMOP Common Data Model. Drug mapping is in particular challenging, because different components have to be mapped: ingredient, dose form and strength. As part of the European Medical Information Framework (EMIF) project, Danish population health data are mapped to the OMOP CDM, including the local drug codes. The Hyve assists in creating a script to automatically map a set of 4754 drugs to the RxNorm vocabulary. The input data contains ATC codes, dosage forms, numerical strengths and strength units. Two examples are shown in Figure 1. The mapping procedure presented here is based on the drug mapping for the Japan Medical Data Center Claims DatabaseI . We empower scientists by building on open source software 2. Mapping Procedure The mapping uses the RxNorm hierarchy and consists of four steps (see Figure 2). 1. Drugs are mapped to RxNorm Ingredient via the 5th level ATC code. The OMOP relationship ‘ATC - RxNorm’ is used for this purpose. 2. Dose form is added to the ingredient level, to map to Clinical Drug Form level. 3. The information on drug strength (including unit) is added to map to Clinical ▲ Figure 1: Examples of input data. Example 1 is successfully mapped automatically. Example 2 consists of two ingredients and has an ATC concept that could not be mapped to a RxNorm concept. ➢ Risperdal ➢ N05AX08 ➢ Filmovertrukne tabletter ➢ 0.5 ➢ MG Example 1 Example 2 ➢ Fortzaar ➢ C09DA06 ➢ depottabletter ➢ 100 + 25 mg Risperidone 0.5 MG Oral Tablet (RxNorm Clinical Drug) Condesartan and diuretics (ATC code) Mapped to
- 48. 48 Use of OMOP/OHDSIprovides EMIF with: u A uniform way to perform suitability and feasibility queries across multiple diverse European data sources u An entry point to quickly initiate and perform observational studies within one or more data sources u Direct insight & dashboarding of data for data owners (e.g. national registries, hospitals)
- 49. The goal ispatient benefit 49 Prof. Johan van der Lei Erasmus MC University Medical Center “We need to learn from experience and find ways to unite the large volumes of data in Europe. At the end of the day, we are in this for better health care.” Co-coordinator EMIF-Platform EMIF-Platform