Department of Medical Informatics, Erasmus MC
Official@mi-erasmusmc
Standardizes pharmaceutical product registries and EMA medicinal presentations into RxNorm clinical terminology for interoperable medical informatics research.
Agent Skills by Department of Medical Informatics, Erasmus MC
Showing 7 vetted skills indexed across 1 GitHub repositories.
mi-erasmusmcmap-latvia-drugs
Map Latvian medicinal product presentations to RxNorm concepts in mapping.tsv files.
mi-erasmusmcprocess-latvia-data
Convert Latvian ZVA registry JSON into sanitized per-product folders with metadata files.
mi-erasmusmcprocess-ema-data
Parse EMA Authorised Presentations PDFs into structured TSV files.
mi-erasmusmcfind-concepts
Search RxNorm concepts via the Hecate semantic search API and return deduplicated concept details.
mi-erasmusmcmap-ema-drugs
Map EMA product presentations to RxNorm concepts in mapping.tsv files.
mi-erasmusmcresolve-conflicts
Find and resolve conflicting RxNorm mappings between EMA and Latvia TSV files.
mi-erasmusmcmap-drugs
Map pharmaceutical product records to RxNorm concepts and validate mapping.tsv outputs.
Frequently Asked Questions About Department of Medical Informatics, Erasmus MC
FAQPage SchemaWhat specific medical informatics tasks are enabled by these capabilities?▼
These capabilities enable the normalization of disparate pharmaceutical datasets into RxNorm clinical terminology. Users can parse EMA PDF presentations, process national ZVA registry records, and perform semantic concept matching to ensure consistent drug identification across international medical databases.
Which technical personas benefit from these medical data processing skills?▼
Medical informaticians, clinical data engineers, and pharmaceutical researchers benefit from these skills. These personas utilize the logic to harmonize multi-source drug registries, resolve mapping discrepancies between regulatory bodies, and prepare structured datasets for clinical decision support systems.
What are the primary dependencies for executing these pharmaceutical mapping tasks?▼
Execution requires access to the Hecate semantic search service for concept retrieval and local availability of source EMA PDF or ZVA JSON registry files. The logic relies on TSV-based mapping structures to validate and store the final pharmaceutical product-to-concept relationships.