Map coded OpenMed clinical facts into OMOP CDM v5.4 records.
npx skills add https://github.com/maziyarpanahi/openmed --skill etl-to-omop-cdm
Please help me install this Agent Skill. Skill: etl-to-omop-cdm Source: https://github.com/maziyarpanahi/openmed/tree/main/skills/etl-to-omop-cdm Command: npx skills add https://github.com/maziyarpanahi/openmed --skill etl-to-omop-cdm
This Skill turns OpenMed-extracted and terminology-coded clinical facts into validated OMOP CDM v5.4 records, reducing the effort required to integrate clinical note NLP with OHDSI and ATLAS analytics.
Use the etl-to-omop-cdm skill to map my coded OpenMed clinical facts into validated OMOP CDM v5.4 condition, drug, and measurement rows using my licensed OHDSI vocabulary bundle.
High-intent search queries and answers about installing and using this skill.
To map clinical NLP facts into OMOP CDM v5.4, this Skill routes coded OpenMed entities into condition_occurrence, drug_exposure, and measurement tables using your OHDSI vocabulary data for standard concept mappings and domain-based routing.
OHDSI ETL workflows require user-supplied vocabulary data including ICD-10-CM, SNOMED, RxNorm, LOINC, source concepts, standard mappings, NLP provenance type concepts, domains, units, and qualitative values to successfully transform clinical note entities.
Yes, coded clinical note entities can be used for ATLAS cohort analytics by converting the extracted NLP facts into validated OMOP CDM rows, which directly support cohort discovery, observational research, and quality measurement.
Clinical event mapping handles unmapped concepts and data quality by applying OHDSI source-to-standard concept patterns, managing de-identified source values, resolving dates, and ensuring required fields and NLP provenance type concepts are accurately populated.
Yes, converting coded clinical NLP facts into OMOP CDM rows requires a user-supplied OHDSI vocabulary bundle, utilizing licensed Athena data for ICD-10-CM, SNOMED, RxNorm, and LOINC standard mappings to ensure terminology standardization.
The best way to integrate clinical note NLP with OHDSI analytics is to apply an ETL process that standardizes extracted entities into OMOP CDM v5.4 condition, drug, and measurement records, reducing manual integration effort for ATLAS studies.
maziyarpanahiConvert OpenMed clinical entity spans into standards-conformant FHIR R4 resources.
monarch-initiativeConvert OHDSI/ATLAS cohort JSON into dismech definitions fragments.
maziyarpanahiAuthor computable OMOP cohort definitions with CIRCE-compatible JSON and NLP augmentation.
BorisDelangeMap source drug concepts to OMOP RxNorm Clinical Drug concepts.
