Mining drug-drug interaction patterns from linked data

A case study for Warfarin, Clopidogrel, and Simvastatin

Jyotishman Pathak, Richard C. Kiefer, Christopher G. Chute

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Citations (Scopus)

Abstract

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.

Original languageEnglish (US)
Title of host publicationProceedings - 2013 IEEE International Conference on Bioinformatics and Biomedicine, IEEE BIBM 2013
Pages23-30
Number of pages8
DOIs
StatePublished - 2013
Event2013 IEEE International Conference on Bioinformatics and Biomedicine, IEEE BIBM 2013 - Shanghai, China
Duration: Dec 18 2013Dec 21 2013

Other

Other2013 IEEE International Conference on Bioinformatics and Biomedicine, IEEE BIBM 2013
CountryChina
CityShanghai
Period12/18/1312/21/13

Fingerprint

Drug interactions
Drug therapy
Health
Semantic Web
Genes

Keywords

  • Drug-Drug Interaction
  • Linked Data
  • Ontologies
  • Semantic Web

ASJC Scopus subject areas

  • Biomedical Engineering

Cite this

Pathak, J., Kiefer, R. C., & Chute, C. G. (2013). Mining drug-drug interaction patterns from linked data: A case study for Warfarin, Clopidogrel, and Simvastatin. In Proceedings - 2013 IEEE International Conference on Bioinformatics and Biomedicine, IEEE BIBM 2013 (pp. 23-30). [6732595] https://doi.org/10.1109/BIBM.2013.6732595

Mining drug-drug interaction patterns from linked data : A case study for Warfarin, Clopidogrel, and Simvastatin. / Pathak, Jyotishman; Kiefer, Richard C.; Chute, Christopher G.

Proceedings - 2013 IEEE International Conference on Bioinformatics and Biomedicine, IEEE BIBM 2013. 2013. p. 23-30 6732595.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Pathak, J, Kiefer, RC & Chute, CG 2013, Mining drug-drug interaction patterns from linked data: A case study for Warfarin, Clopidogrel, and Simvastatin. in Proceedings - 2013 IEEE International Conference on Bioinformatics and Biomedicine, IEEE BIBM 2013., 6732595, pp. 23-30, 2013 IEEE International Conference on Bioinformatics and Biomedicine, IEEE BIBM 2013, Shanghai, China, 12/18/13. https://doi.org/10.1109/BIBM.2013.6732595
Pathak J, Kiefer RC, Chute CG. Mining drug-drug interaction patterns from linked data: A case study for Warfarin, Clopidogrel, and Simvastatin. In Proceedings - 2013 IEEE International Conference on Bioinformatics and Biomedicine, IEEE BIBM 2013. 2013. p. 23-30. 6732595 https://doi.org/10.1109/BIBM.2013.6732595
Pathak, Jyotishman ; Kiefer, Richard C. ; Chute, Christopher G. / Mining drug-drug interaction patterns from linked data : A case study for Warfarin, Clopidogrel, and Simvastatin. Proceedings - 2013 IEEE International Conference on Bioinformatics and Biomedicine, IEEE BIBM 2013. 2013. pp. 23-30
@inproceedings{46289a1b423843e3ac64d9bf2d7112dc,
title = "Mining drug-drug interaction patterns from linked data: A case study for Warfarin, Clopidogrel, and Simvastatin",
abstract = "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.",
keywords = "Drug-Drug Interaction, Linked Data, Ontologies, Semantic Web",
author = "Jyotishman Pathak and Kiefer, {Richard C.} and Chute, {Christopher G.}",
year = "2013",
doi = "10.1109/BIBM.2013.6732595",
language = "English (US)",
isbn = "9781479913091",
pages = "23--30",
booktitle = "Proceedings - 2013 IEEE International Conference on Bioinformatics and Biomedicine, IEEE BIBM 2013",

}

TY - GEN

T1 - Mining drug-drug interaction patterns from linked data

T2 - A case study for Warfarin, Clopidogrel, and Simvastatin

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

SN - 9781479913091

SP - 23

EP - 30

BT - Proceedings - 2013 IEEE International Conference on Bioinformatics and Biomedicine, IEEE BIBM 2013

ER -