TY - GEN
T1 - Mining anti-coagulant drug-drug interactions from electronic health records using linked data
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 (PDDI) 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 PDDIs for widely prescribed anti-coagulant Warfarin. Our results from the proof-of-concept study demonstrate the potential of applying such a methodology to study prescription trends based on gender and age as well as patient health outcomes.
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 (PDDI) 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 PDDIs for widely prescribed anti-coagulant Warfarin. Our results from the proof-of-concept study demonstrate the potential of applying such a methodology to study prescription trends based on gender and age as well as patient health outcomes.
KW - Drug-drug interactions
KW - DrugBank
KW - Electronic Health Records
KW - Federated querying
KW - SPARQL
UR - http://www.scopus.com/inward/record.url?scp=84879905974&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84879905974&partnerID=8YFLogxK
U2 - 10.1007/978-3-642-39437-9_11
DO - 10.1007/978-3-642-39437-9_11
M3 - Conference contribution
AN - SCOPUS:84879905974
SN - 9783642394362
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 128
EP - 140
BT - Data Integration in the Life Sciences - 9th International Conference, DILS 2013, Proceedings
T2 - 9th International Conference on Data Integration in the Life Sciences, DILS 2013
Y2 - 11 July 2013 through 12 July 2013
ER -