using semantic web technologies to reproduce a pharmacovigilance case study
DESCRIPTION
We show how the use of PROV-O helps to analyse, discuss and communicate the reconstruction of a scientific workflow of a pharmacovigilance paper.TRANSCRIPT
Using Semantic Web Technologies to Reproduce
a Pharmacovigilance Case Study
Michiel Hildebrand, Rinke Hoekstra & Jacco van Ossenbruggen
computational (open) data study
(open) data process results
prov:Entityprov:Entity prov:Entityprov:Entityprov:Activityprov:Activity
pharmacovigilance
detect side effects of drugs: disproportional correlation between a drug and an associated adverse event
prov:Entityprov:Entity prov:Entityprov:Entityprov:Activityprov:Activity
2x2 contingency table
28.887
computation is never trivialcomputation is never trivial
28.663
28.767
28.862
28.86228.837
?PROV helps to communicatePROV helps to communicate
reproduction
2,231,038+9
1,664,078-142
?all drug names were unified into generic names by a text-mining approach. Spelling errors were detected by GNU Aspell and carefully confirmed by working pharmacists.
3.525+1
23,865,029+1,847,073
Foods beverages, treatments (e.g. X-ray radiation), and unspecified names (e.g. beta-blockers) were omitted
debugging requires intermediate datasetsdebugging requires intermediate datasets
reproduction
original
PRR = 2.520
PRR = 2.504
PROV helps to communicate
>> share your provenance graph
PROV helps to communicate
>> share your provenance graph
debugging requires intermediate datasets
>> share each prov:Entity
debugging requires intermediate datasets
>> share each prov:Entity
computation is never trivial(applies also to “preprocessing” & “well known” formula’s)
>> share each computational prov:Activity
computation is never trivial(applies also to “preprocessing” & “well known” formula’s)
>> share each computational prov:Activity