queryMed: Semantic Web functions for linking pharmacological and medical knowledge to data

Area of Science:

• Bioinformatics
• Public Health Informatics
• Computational Biology

Background:

• Biomedical knowledge integration is crucial for analyzing electronic health data.
• Existing methods for patient selection and event identification in public health research often require extensive medical expertise.
• Leveraging linked data formats like ontologies can enhance the interpretability of health information.

Purpose of the Study:

• To introduce the queryMed R package for integrating medical and pharmacological knowledge into R.
• To demonstrate queryMed's utility in identifying drug prescriptions and detecting contraindications within a critical limb ischemia patient cohort.
• To facilitate the use of linked data (ontologies, RDF) in public health research using the R statistical environment.

Main Methods:

• Developed the queryMed R package to process and integrate biomedical knowledge from linked data sources.
• Utilized OWL ontologies and RDF datasets for medical and pharmacological information.
• Linked a critical limb ischemia (CLI) patient database with ontologies to analyze prescription data.

Main Results:

• Successfully identified all drugs prescribed for critical limb ischemia (CLI) patients.
• Detected a contraindicated drug prescription for a patient by linking clinical data with medical ontologies.
• queryMed demonstrated its capability for medical data mapping and enrichment.

Conclusions:

• The queryMed R package simplifies the integration of biomedical knowledge into R for public health research.
• queryMed enables automated identification of drug information and potential prescription issues, reducing the need for manual expert review.
• This tool enhances the analysis of electronic health data by leveraging semantic web technologies.