adepedia 2.0-integration of normalized adverse drug events (ades)
TRANSCRIPT
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ADEpedia 2.0: Integration of
Normalized Adverse Drug Events(ADEs) Knowledge from the UMLS
Guoqian Jiang, MD, PhD
Mayo Clinic2013 AMIA Summit on Clinical Research InformaticsMarch 22, 2013
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Acknowledgements
Co-authorsHongfang Liu, PhDHarold R. SolbrigChristopher G. Chute, MD. Dr. PH
This work was supported in part by the SHARP Area 4: Secondary Use of EHR
Data (90TR000201)
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Introduction
Adverse drug events (ADEs) are a well-recognizedcause of patient morbidity and increased healthcare costs in the United States.
Traditionally, spontaneous reporting is used as themain source for drug safety surveillance.
Notably, the US Food and Drug Administration(FDA) uses an adverse event reporting system(AERS) to monitor for new adverse events and
medication errors that might occur with allapproved drug and therapeutic biologicproducts
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Introduction
To facilitate the ADE reporting and detection,there is emerging interest in secondary use ofclinical data from the electronic medical records(EMRs).
A fundamental challenge is that the communitylacks a publicly-available, standardized ADEknowledge base that encodes known ADE
information for drug surveillance.
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Introduction
The known ADE information is very useful for avoidingover-alerting of ADE signals detected by data miningalgorithms, and reducing pharmacovigilance study noiselevels, so that only novel signals are considered for
further exploration.ADEpedia: a standardized knowledge base of ADEs for
drug safety surveillance
FDA Structured Product Labeling (SPL), FDA Adverse Event Reporting System (AERS) and the Unified Medical Language System (UMLS)
http://adepedia.org
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Introduction
The Unified Medical Language System (UMLS),developed by the National Library of Medicine(NLM), intends to promote creation of moreeffective and interoperable biomedicalinformation system and services [12].
A systematic review and organization of knownADE knowledge from the UMLS would be a
good starting point to facilitate the integration ofknown ADE knowledge and ultimately form acomprehensive ADE knowledge base.
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Co-occurrence
Co-occurrence is one of essential statistics that hasbeen widely used in ADE text mining and knowledgeacquisition from biomedical and clinical documents.
Wright et al. used co-occurrence-based associationrule mining technique to identify a large number ofclinical accurate associations that may be useful for
identifying probable gaps in the problem list. Liu Y, et al demonstrated an approach that
statistically significant co-occurrence of drug-diseasementions in the clinical notes can be used to detect
ADE signals .
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The objectives of this study
To extract and normalize ADE knowledge fromthe UMLS and integrate the ADE knowledgewith the ADEpedia; and
To enrich the knowledge base with the drug-disorder co-occurrence data from a large-scaleEMR system.
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Knowledge Resources for ADE Integration
1) UMLS 2011AB: We downloaded and installed the version of UMLS
2011AB.
2) Semantic groups: We used 2 UMLS semantic groups Chemicals &
Drugs (CHEM) and Disorders (DISO), and theircorresponding semantic types.
3) ADE data from the ADEpedia. In the knowledge base, the medication data are
represented by RxNorm codes and the ADE data arerepresented by SNOMED CT and MedDRA codes.
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Electronic Medical Records (EMRs) DataSources
We used the UMLS CUI co-occurrence datathat were extracted from a 51-million-documentcorpus of clinical text covering Mayo Clinicclinical notes between 1/1/2001-12/31/2010 in a
previous study.
The clinical notes were originally retrieved fromthe Mayos Enterprise Data Trust (EDT), which
is a comprehensive snapshot of Mayo Clinicsservice areas and includes structured data,unstructured text, and Clinical Notes Indexing(CNI)-produced annotations.
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Methods
Profiled the Drug-Disorder Pairs andRelationships in the UMLS
Chemicals & Drugs Disorders
Enriched the UMLS ADE data with the EMR co-occurrence statistics
Evaluated the Utility of the UMLS ADE Data
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Profiling Results of ADE Knowledge in theUMLS
We extracted 266,832 concept pairs betweenthe two concept sets, covering 14,256 (1.69%)concepts from the group Chemicals & Drugsand 19,006 (3.53%) concepts from the group
Disorders.
We profiled the contribution of sourcevocabularies to the concept pairs. In total, there
are 47 source vocabularies having thecontribution.
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Distribution of drug-disorder concept pairscontributed by source vocabularies (top 20)
0.3%
0.3%
0.4%
0.4%
0.5%
0.6%
0.6%
0.6%
0.6%
0.6%
0.6%
0.6%
0.7%
0.7%
0.7%
2.1%
3.1%
7.3%
7.3%
70.4%
0.0% 10.0% 20.0% 30.0% 40.0% 50.0% 60.0% 70.0% 80.0%
MSHSPA
CCPSS
MSH
MSHSCR
MEDCIN
MSHITA
MSHPOL
MSHRUS
MSHCZE
MSHGER
MSHFRE
MSHFIN
NCI
MSHSWE
MEDLINEPLUS
SNMI
MTH
SCTSPA
SNOMEDCT
NDFRT
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Profiling results of relationships betweendrugs and disorders
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Co-occurrence Enrichment Using theEMRs
Drug-disorder concept pairs identified fromUMLS
Out of 266,832 concept pairs, 71,626concept pairs co-occurred in the EMRs,accounting for 26.8%.
Drug-disorder concept pairs from the ADEpediaOf 299,476 drug-disorder concept pairs,
100,577 concept pairs co-occurred in theEMRs, accounting for 33.6%.
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Utility Evaluation and Case Study
We identified 2,164 CUIs overlapping across the twodata sets (ADEpedia vs. UMLS), accounting for 56.5%of RxNorm Drugs in the ADEpedia.
The 2,164 CUIs linked with 66,058 drug-disorder pairsfrom the UMLS.
The result indicated that the UMLS is a useful resourcefrom the perspective of its integration with the
ADEpedia.
As a case study, we selected a drug called Digoxin,which is indicated for the treatment of congestive heartfailure.
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ADE knowledge extracted from theUMLS for the drug Digoxin|C0012265
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Discussion
The profiling results of the UMLS clearlydemonstrated that the drug-disorder associationsin the UMLS are largely underspecified.
Only 1.69% (14,256) concepts from the groupChemicals & Drugs and 3.53% (19,006)concepts from the group Disorders in theUMLS had drug-disorder associationsasserted.
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Discussion
From the perspective of ADE detectionapplication, we classified the assertedrelationships between the drug-disorder pairsinto 4 categories.
This kind of categorization would provideaggregation capability for the knowledge sourceand improve its utility for the purpose of drug
surveillance.
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Discussion
We enriched the knowledge base with thestatistical co-occurrence information extractedfrom the EMRs.
The co-occurrence information extracted from a51-million-document EMR system will be usefulfor validating ADE detection algorithm acrossclinical institutions and across text corpora.
In future, we will explore the characteristics ofthe UMLS term occurrences, focusing on theADE detection use case.
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Summary
The UMLS is a useful source for providing standardizedADE knowledge relevant to indications, contraindicationsand adverse effects, and complementary to the ADE datafrom drug product labels.
The statistics enrichment from EMRs would potentiallyenable the meaningful use of ADE data for drug safetysurveillance.
The EMR-enriched ADE dataset is available for downloadat the ADEpedia website (http://adepedia.org).
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ADEKnowledge
From EMRs
Standardized ADE Knowledge Base
(RDF Store)
ADE
Data
From
SPLs
ADEKnowledge
From
UMLS
Semantic Web RESTful Services(JSON, XML, RDF, TTL, etc.)
ADE Detection / Drug SurveillanceApplications
Data Layer
KnowledgeBase Layer
ServiceLayer
ApplicationLayer
Other ADEResources
(AERS)
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Questions & Discussion