TY - GEN
T1 - Using linked data for mining drug-drug interactions in electronic health records
AU - Pathak, Jyotishman
AU - Kiefer, Richard C.
AU - Chute, Christopher G.
PY - 2013
Y1 - 2013
N2 - 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.
AB - 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.
KW - Drug-drug interactions
KW - Electronic health records
KW - Federated querying
KW - Semantic Web
UR - http://www.scopus.com/inward/record.url?scp=84894333282&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84894333282&partnerID=8YFLogxK
U2 - 10.3233/978-1-61499-289-9-682
DO - 10.3233/978-1-61499-289-9-682
M3 - Conference contribution
C2 - 23920643
AN - SCOPUS:84894333282
SN - 9781614992882
T3 - Studies in Health Technology and Informatics
SP - 682
EP - 686
BT - MEDINFO 2013 - Proceedings of the 14th World Congress on Medical and Health Informatics
PB - IOS Press
T2 - 14th World Congress on Medical and Health Informatics, MEDINFO 2013
Y2 - 20 August 2013 through 23 August 2013
ER -