Using linked data for mining drug-drug interactions in electronic health records.
By nature, healthcare data is highly complex and voluminous. While on one hand, it provides unprecedented opportunities to identify hidden and unknown relationships between patients and treatment outcomes, or drugs and allergic reactions for given individuals, representing and querying large network datasets poses significant technical challenges. In this research, we study the use of Semantic Web and Linked Data technologies for identifying drug-drug interaction (DDI) information from publicly available resources, and determining if such interactions were observed using real patient data. Specifically, we apply Linked Data principles and technologies for representing patient data from electronic health records (EHRs) at Mayo Clinic as Resource Description Framework (RDF), and identify potential drug-drug interactions (PDDIs) for widely prescribed cardiovascular and gastroenterology drugs. Our results from the proof-of-concept study demonstrate the potential of applying such a methodology to study patient health outcomes as well as enabling genome-guided drug therapies and treatment interventions.
Department of Health Sciences Research, Mayo Clinic, Rochester, MN, USA.
Jyotishman
J
Pathak
Richard C
RC
Kiefer
Christopher G
CG
Chute
D016907:Adverse Drug Reaction Reporting Systems; D001185:Artificial Intelligence; D057225:Data Mining / Q000379:methods*; D004347:Drug Interactions; D064420:Drug-Related Side Effects and Adverse Reactions / Q000145:classification*; D057286:Electronic Health Records; D020407:Internet; D008498:Medical Record Linkage / Q000379:methods*; D009323:Natural Language Processing; D012660:Semantics