TY - GEN
T1 - Mining drug-drug interaction patterns from linked data
T2 - 2013 IEEE International Conference on Bioinformatics and Biomedicine, IEEE BIBM 2013
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 potential 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) graphs, and identify potential DDIs for three widely prescribed cardiovascular drugs: Warfarin, Clopidogrel and Simvastatin. 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 potential 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) graphs, and identify potential DDIs for three widely prescribed cardiovascular drugs: Warfarin, Clopidogrel and Simvastatin. 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 Interaction
KW - Linked Data
KW - Ontologies
KW - Semantic Web
UR - http://www.scopus.com/inward/record.url?scp=84894545011&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84894545011&partnerID=8YFLogxK
U2 - 10.1109/BIBM.2013.6732595
DO - 10.1109/BIBM.2013.6732595
M3 - Conference contribution
AN - SCOPUS:84894545011
SN - 9781479913091
T3 - Proceedings - 2013 IEEE International Conference on Bioinformatics and Biomedicine, IEEE BIBM 2013
SP - 23
EP - 30
BT - Proceedings - 2013 IEEE International Conference on Bioinformatics and Biomedicine, IEEE BIBM 2013
Y2 - 18 December 2013 through 21 December 2013
ER -