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

Abstract

Summary In public health research and more precisely in the reuse of electronic health data, selecting patients, identifying specific events and interpreting results typically requires biomedical knowledge. The queryMed R package aims to facilitate the integration of medical and pharmacological knowledge stored in formats compliant with the Linked Data paradigm (e.g. OWL ontologies and RDF datasets) into the R statistical programming environment. We show how it allowed us to identify all the drugs prescribed for critical limb ischemia (CLI) and also to detect one contraindicated prescription for one patient by linking a medical database of 1003 CLI patients to ontologies. Availability and implementation queryMed is readily usable for medical data mappings and enrichment. Sources, R vignettes and test data are available on GitHub (https://github.com/yannrivault/queryMed) and are archived on Zenodo (https://doi.org/10.5281/zenodo.1323481).

Semantic Web in Healthcare: A Systematic Literature Review of Application, Research Gap, and Future Research Avenues

“…ey help to select patients, recognize drug effects, and analyze results by using electronic health data from numerous sources. e queryMed packages were proposed for pharmaco-epidemiologists that link medical and pharmacological knowledge with electronic health records [10].…”

“…(iv) QueryMed R package made the integration of clinical and pharmacological information that is used to distinguish all the medications endorsed for critical limb ischemia (CLI) and to recognize one contraindicated solution for one patient [10].…”

“…(iii) No prior work is done in expanding the set of prebuilt queries of a particular disease to handle a wide range of use cases through possible linked data evolutions [10].…”

“…(i) Which policies and regulations may ontological systems use to comply with W3C standards [20]? (ii) How can scholars expand a disease's set of queries to cover a wider range of use cases [10]? (iii) What will be the most effective user interface designs for massive data networks that can interpret the semantics of clinical data [74]?…”

“…The increasing number of knowledge grounds, heterogeneity of schema representation, and lack of conceptual description make the processing of these knowledge bases complicated. Non-experts find mixing knowledge with patient databases challenging to facilitate data sharing [ 10 ]. Similarly, ensuring the certainty of disease diagnosis also becomes a more significant challenge for health providers.…”